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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 8 | Copyright
Hydrol. Earth Syst. Sci., 14, 1567-1579, 2010
https://doi.org/10.5194/hess-14-1567-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  17 Aug 2010

17 Aug 2010

A method for parameterising roughness and topographic sub-grid scale effects in hydraulic modelling from LiDAR data

A. Casas1,4, S. N. Lane2, D. Yu3, and G. Benito1 A. Casas et al.
  • 1Institute of Natural Resources, Consejo Superior de Investigaciones Científicas, 28006 Madrid, Spain
  • 2Institute Hazard, Risk and Resilience and Department of Geography, Durham University, Durham, DH1 3LE, UK
  • 3Department of Geography, Loughborough University, Loughborough, LE11 3TU, UK
  • 4Department of Land, Air, and Water Resources, University of California, Davis, CA 95616, USA

Abstract. High resolution airborne laser data provide new ways to explore the role of topographic complexity in hydraulic modelling parameterisation, taking into account the scale-dependency between roughness and topography. In this paper, a complex topography from LiDAR is processed using a spatially and temporally distributed method at a fine resolution. The surface topographic parameterisation considers the sub-grid LiDAR data points above and below a reference DEM, hereafter named as topographic content. A method for roughness parameterisation is developed based on the topographic content included in the topographic DEM. Five subscale parameterisation schemes are generated (topographic contents at 0, ±5, ±10, ±25 and ±50 cm) and roughness values are calculated using an equation based on the mixing layer theory (Katul et al., 2002), resulting in a co-varied relationship between roughness height and topographic content. Variations in simulated flow across spatial subscales show that the sub grid-scale behaviour of the 2-D model is not well-reflected in the topographic content of the DEM and that subscale parameterisation must be modelled through a spatially distributed roughness parameterisation. Variations in flow predictions are related to variations in the roughness parameter. Flow depth-derived results do not change systematically with variation in roughness height or topographic content but they respond to their interaction. Finally, subscale parameterisation modifies primarily the spatial structure (level of organisation) of simulated 2-D flow linearly with the additional complexity of subscale parameterisation.

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