Articles | Volume 14, issue 12
Hydrol. Earth Syst. Sci., 14, 2661–2669, 2010
https://doi.org/10.5194/hess-14-2661-2010

Special issue: Observing and modeling the catchment-scale water cycle

Hydrol. Earth Syst. Sci., 14, 2661–2669, 2010
https://doi.org/10.5194/hess-14-2661-2010

  22 Dec 2010

22 Dec 2010

Aerodynamic roughness length estimation from very high-resolution imaging LIDAR observations over the Heihe basin in China

J. Colin1 and R. Faivre1,2 J. Colin and R. Faivre
  • 1Image Science, Computer Science and Remote Sensing Laboratory, UMR 7005 CNRS – University of Strasbourg, BP10413, 67412 ILLKIRCH Cedex, France
  • 2Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629HS, Delft, The Netherlands

Abstract. Roughness length of land surfaces is an essential variable for the parameterisation of momentum and heat exchanges. The growing interest in the estimation of the surface turbulent flux parameterisation from passive remote sensing leads to an increasing development of models, and the common use of simple semi-empirical formulations to estimate surface roughness. Over complex surface land cover, these approaches would benefit from the combined use of passive remote sensing and land surface structure measurements from Light Detection And Ranging (LIDAR) techniques. Following early studies based on LIDAR profile data, this paper explores the use of imaging LIDAR measurements for the estimation of the aerodynamic roughness length over a heterogeneous landscape of the Heihe river basin, a typical inland river basin in the northwest of China. The point cloud obtained from multiple flight passes over an irrigated farmland area were used to separate the land surface topography and the vegetation canopy into a Digital Elevation Model (DEM) and a Digital Surface Model (DSM) respectively. These two models were then incorporated in two approaches: (i) a strictly geometrical approach based on the calculation of the plan surface density and the frontal surface density to derive a geometrical surface roughness; (ii) a more aerodynamic approach where both the DEM and DSM are introduced in a Computational Fluid Dynamics model (CFD). The inversion of the resulting 3-D wind field leads to a fine representation of the aerodynamic surface roughness. Examples of the use of these three approaches are presented for various wind directions together with a cross-comparison of results on heterogeneous land cover and complex roughness element structures.