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

Abstract

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.

Through‐water terrestrial laser scanning of gravel beds at the patch scale

“…Numerous studies examine the properties of this boundary on account of its role in flow hydraulics (roughness effects on resistance and turbulence; Lacey and Roy, ), sediment transport (resistance to entrainment and partitioning of near‐bed stresses; Petit et al ., ) and ecological processes (invertebrate crawl patterns and the presence of instream flow refugia; Lancaster et al ., ; Gibbins et al ., ). Furthermore, numerical simulation models of flow over rough surfaces are sensitive to the parameterization of the topographic boundary (Casas et al ., ). High‐resolution datasets are essential to establish the elements of fine‐scale topography and flow processes that must be parameterized in such models.…”

Use of the 3-D scanner in mapping and monitoring the dynamic degradation of soils: case study of the Cucuteni-Baiceni Gully on the Moldavian Plateau (Romania)

“…Microform relief leads to significant losses of soil and causes mal gravitational processes (Radoane et al, 1999). Gullying processes triggering causes are found primarily in the nature of the geological substratum of the landscape, climate change, pollution phenomena and anthropogenic interventions (Blong et al, 1982;Casas et al, 2010;Ernst, 2010;Heede, 1976).…”

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Representing hydrodynamically important blocking features in coastal or riverine lidar topography

“…A potential area where the edge-blocking method might be expanded is in the estimation of topographic roughness, which has been a subject of extensive prior research (e.g., Abu-Aly et al, 2014;Casas et al, 2010;Dorn et al, 2014;Forzieri et al, 2011;Straatsma and Baptist, 2008). By defining edge features, a portion of the difference between the grid cell elevation and subgrid features can be removed from the roughness estimation; i.e., we could use, for example, G xx , G yy , H xx , and H yy to remove pixels that have been resolved in to edge features and only consider the remaining pixels in a coarse-grid cell as contributing to roughness.…”