LIDAR DATA RESOLUTION VERSUS HYDRO-MORPHOLOGICAL MODELS FOR FLOOD RISK ASSESSMENT

Abstract. Uncertainties in topographic data have a significant influence on hydro-morphological and hydraulic predictions and therefore on flood risk assessment. In this work, the effects of topographic data resolution on the results of hydro-morphologic and hydraulic simulations are analysed using respectively the morphological bi-dimensional curvilinear model MIKE 21C and mono-bidimensional SOBEK. The studies have been carried out in the Torre river, located in Northern Italy. The evaluations on hydro-morphological and hydraulic risk require accurate spatial information for the area of interest. In order to characterize the river morphology, mainly for large areas, the availability of high resolution topography derived by airborne laser scanner represents an effective tool. Nowadays LiDAR (Light Detection And Ranging) DTM covering large areas are readily available for public authorities, and there is a greater and more widespread interest in the application of such information for the development of automated methods aimed at solving geomorphological and hydrological problems. For the models analysed, high-resolution LiDAR data were used to create the basic topographic information. An additional source for the topography of the Torre river has been provided by river cross-section data. Digital elevation models at different resolution have been created to test the effects of different grid cell sizes on the simulations. The impact of topographic information on hydraulic and morphological model results was evaluated for the area through a comparison of results. Additionally, morphologic variations and position of erosional and depositional zones along the watercourse and variations in flood extent and dynamics have been investigated. The obtained results emphasise the importance of quality of input information for reliable results of hydraulic and sediment transport models. Criteria for selecting the optimal DTM resolution are suggested, based on the quality of available data.