TOWARDS A HIGH-RESOLUTION DRONE-BASED 3D MAPPING DATASET TO OPTIMISE FLOOD HAZARD MODELLING
- 1Research Unit Engineering Science, University of Luxembourg, 6 rue Richard Coudenhove-Kalergi, L-1359 Luxembourg
- 2RSS-Hydro Sarl-S, Dudelange Innovation Hub, route de Volmerange, L-3593 Dudelange, Luxembourg
- 3School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
- 4Dept. of Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT, UK
Keywords: Urban flood modelling, DSM, Drone Photogrammetry
Abstract. The occurrence of urban flooding following strong rainfall events may increase as a result of climate change. Urban expansion, aging infrastructure and an increasing number of impervious surfaces are further exacerbating flooding. To increase resilience and support flood mitigation, bespoke accurate flood modelling and reliable prediction is required. However, flooding in urban areas is most challenging. State-of-the-art flood inundation modelling is still often based on relatively low-resolution 2.5 D bare earth models with 2–5 m GSD. Current systems suffer from a lack of precise input data and numerical instabilities and lack of other important data, such as drainage networks. Especially, the quality and resolution of the topographic input data represents a major source of uncertainty in urban flood modelling. A benchmark study is needed that defines the accuracy requirements for highly detailed urban flood modelling and to improve our understanding of important threshold processes and limitations of current methods and 3D mapping data alike.
This paper presents the first steps in establishing a new, innovative multiscale data set suitable to benchmark urban flood modelling. The final data set will consist of high-resolution 3D mapping data acquired from different airborne platforms, focusing on the use of drones (optical and LiDAR). The case study includes residential as well as rural areas in Dudelange/Luxembourg, which have been prone to localized flash flooding following strong rainfall events in recent years. The project also represents a cross disciplinary collaboration between the geospatial and flood modelling community. In this paper, we introduce the first steps to build up a new benchmark data set together with some initial flood modelling results. More detailed investigations will follow in the next phases of this project.