Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 13 8 2009 10.5194/hess-13-1453-2009 http://www.hydrol-earth-syst-sci.net/13/1453/2009/ http://www.hydrol-earth-syst-sci.net/13/1453/2009/hess-13-1453-2009.html http://www.hydrol-earth-syst-sci.net/13/1453/2009/hess-13-1453-2009.pdf 1453 1466 2009-08-14 Optimisation of LiDAR derived terrain models for river flow modelling G. Mandlburger gm@ipf.tuwien.ac.at C. Hauer B. Höfle H. Habersack N. Pfeifer Christian Doppler Laboratory "Spatial Data from Laser Scanning and Remote Sensing" at the Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Gusshausstrasse 27–29, 1040 Vienna, Austria Institute of Water Management, Hydrology and Hydraulic Engineering, BOKU – University of Applied Life Sciences, Muthgasse 107, 1190 Vienna, Austria Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Gusshausstrasse 27–29, 1040 Vienna, Austria Airborne LiDAR (Light Detection And Ranging) combines cost efficiency, high degree of automation, high point density of typically 1–10 points per m² and height accuracy of better than ±15 cm. For all these reasons LiDAR is particularly suitable for deriving precise Digital Terrain Models (DTM) as geometric basis for hydrodynamic-numerical (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. 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