Towards a High-Resolution Drone-Based 3d Mapping Dataset to Optimise Flood Hazard Modelling

Abstract

<p><strong>Abstract.</strong> 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.</p><p>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.</p>

Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives

“…High‐resolution DTMs derived from UAV imagery have been used in flood prediction models (e.g., LISFLOOD‐FP) to simulate water flows and depths (Backes et al., 2019) and calibrate and validate satellite‐based products (Vivoni et al., 2014). One such example is the use of drone imagery in training classification algorithms and validation of satellite imagery in estuarine wetlands in the Rachel Carson Reserve in Beaufort, NC, USA (Gray et al., 2018).…”

“…UAVs and low‐altitude photogrammetry have been consistently applied in hydrology to develop topographical models (Lowe et al., 2019; Resop et al., 2019) and optimize natural hazards modeling (Backes et al., 2019). UAVs improve DEM and vegetation classification with reduced survey time and cost for hydrological measurements and modeling, including water stress, floods, and landslides.…”

Accuracy Assessment of Surveying Strategies for the Characterization of Microtopographic Features That Influence Surface Water Flooding

“…Previous studies indicate that microtopography [10,11] is a critical factor influencing the extent and risk of local flooding. It influences the flow path, particularly during shallow water flow associated with urban SWF [7,[12][13][14]. Microtopography guides the surface water runoff flow direction and velocity and impacts the flood extent and depth.…”

“…The results showed that the UAS-based model (RMSE = 0.75 m) outperformed the RTK-GPS (RMSE = 0.83 m) for the estimation of maximum water depth. Backes et al [14] investigated the effect of using UAS highresolution data, with an average Ground Sampling Distance (GSD) of 2 cm, to achieve accurate flood predictions. The study confirmed the impact of microtopographic features on flow, pooling and water depth and identified the importance of investigating it in further detail.…”

UAV-DEMs for Small-Scale Flood Hazard Mapping

“…Leitão et al [41] performed a sensitivity analysis of the UAVs flight parameters on the accuracy of the UAV-generated DEM and obtained limited Elevation Differences (ED) with 2 m resolution LiDAR (mean ED equal to 0.06 m). There are successful applications of UAV-derived DEM as topographic input for short rainstorm modeling [42], channel reconstruction, and flood modeling for reproducing real flood events [43,44], showing some limitations mostly in highly vegetated areas (e.g., along river banks). Hashemi-Beni et al 2018 [40] adopted UAVs for water surface detection after a flood event, providing results in agreement with the ones obtained from LiDAR DEM.…”