Optimisation of LiDAR derived terrain models for river flow modelling

Abstract

Abstract. Airborne LiDAR (Light Detection And Ranging) combines cost efficiency, high degree of automation, high point density of typically 1-10 points per m 2 and height accuracy of better than ±15 cm. For all these reasons Li-DAR is particularly suitable for deriving precise Digital Terrain Models (DTM) as geometric basis for hydrodynamicnumerical (HN) simulations. The application of LiDAR for river flow modelling requires a series of preprocessing steps. Terrain points have to be filtered and merged with river bed data, e.g. from echo sounding. Then, a smooth Digital Terrain Model of the Watercourse (DTM-W) needs to be derived, preferably considering the random measurement error during surface interpolation. In a subsequent step, a hydraulic computation mesh has to be constructed. Hydraulic simulation software is often restricted to a limited number of nodes and elements, thus, data reduction and data conditioning of the high resolution LiDAR DTM-W becomes necessary. We will present a DTM thinning approach based on adaptive TIN refinement which allows a very effective compression of the point data (more than 95% in flood plains and up to 90% in steep areas) while preserving the most relevant topographic features (height tolerance ±20 cm). Traditional hydraulic mesh generators focus primarily on physical aspects of the computation grid like aspect ratio, expansion ratio and angle criterion. They often neglect the detailed shape of the topography as provided by LiDAR data. In contrast, our approach considers both the high geometric resolution of Correspondence to: G. Mandlburger (gm@ipf.tuwien.ac.at) the LiDAR data and additional mesh quality parameters. It will be shown that the modelling results (flood extents, flow velocities, etc.) can vary remarkably by the availability of surface details. Thus, the inclusion of such geometric details in the hydraulic computation meshes is gaining importance in river flow modelling.

Social media as an information source for rapid flood inundation mapping

“…Further, the potential difference of the location of the photo and the geolocation of the tweet, as well as the offset from the location of where the photo was taken and the photo contents, involves uncertainties concerning the horizontal location. Improvements concerning the vertical and horizontal accuracy of inundation depth estimates can be expected from using higher resolution lidar DEMs which may achieve vertical accuracies for terrain data in the range of ±15 cm (Mandlburger et al, 2009) and thus also include details about dike crests. Improvements can also be expected as a result of the integration of more detailed information about the reference environment as for instance available from 3-D-city models (Gröger et al, 2012).…”

“…This particularly concerns the accuracy of ground levels as well as the realistic representation of hinterland flow paths and flood protection schemes, since these details locally control flooding. The advent of airborne laser altimetry, for instance lidar, has significantly improved the resolution and vertical accuracy of DEMs within the lower range of decimeters (Mandlburger et al, 2009;Bates, 2012).…”

Channel Representation in Physically Based Models Coupling Groundwater and Surface Water: Pitfalls and How to Avoid Them

“…They may be time consuming or expensive, yet they favor repeatability and some consistency between different datasets. Potential limitations include the high data volume, which must be reduced to a manageable size (Mandlburger et al 2009), and the poor representation of hydraulically significant steep slopes, despite seemingly small sampling intervals (Heritage et al 2009). …”

“…Designing the mesh of a river network is generally a challenging optimization problem where the number of nodes must be minimized, while process representation and model convergence must be ensured (Mandlburger et al 2009;Caviedes-Voullième et al 2012). One approach is the examination of grid convergence.…”

“…The variogram range or a visual inspection of the topography can provide a general criterion to select an initial mesh size, the key step being the discrimination between features that need to be explicitly incorporated into the model, and those that are implicitly expressed through parameterization . Reliable stream flow simulations can generally be achieved by elongating elements in the direction of flow (Mandlburger 2006;Mandlburger et al 2009), while shortening them as a function of local curvature (Horritt 2000).…”