Effects of bathymetric lidar errors on flow properties predicted with a multi-dimensional hydraulic model

Abstract

New remote sensing technologies and improved computer performance now allow numerical flow modeling over large stream domains. However, there has been limited testing of whether channel topography can be remotely mapped with accuracy necessary for such modeling. We assessed the ability of the Experimental Advanced Airborne Research Lidar, to support a multi-dimensional fluid dynamics model of a small mountain stream. Random point elevation errors were introduced into the lidar point cloud, and predictions of water surface elevation, velocity, bed shear stress, and bed mobility were compared to those made without the point errors. We also compared flow model predictions using the lidar bathymetry with those made using a total station channel field survey. Lidar errors caused < 1 cm changes in the modeled water surface elevations. Effects of the point errors on other flow characteristics varied with both the magnitude of error and the local spatial density of lidar data. Shear stress errors were greatest where flow was naturally shallow and fast, and lidar errors caused the greatest changes in flow cross-sectional area. The majority of the stress errors were less than ± 5 Pa. At near bankfull flow, the predicted mobility state of the median grain size changed over ≤ 1.3% of the model domain as a result of lidar elevation errors and ≤ 3% changed mobility in the comparison of lidar and ground-surveyed topography. In this riverscape, results suggest that an airborne bathymetric lidar can map channel topography with sufficient accuracy to support a numerical flow model.

Topo-Bathymetric LiDAR for Monitoring River Morphodynamics and Instream Habitats—A Case Study at the Pielach River

“…For the latter, RMSE values in the range of 10-25 cm are reported comparing the bathymetric LiDAR with RTK GPS reference points. The effects of bathymetric LiDAR errors on the flow properties derived from a multi-dimensional fluid dynamics model were assessed in [56] for the Experimental Advanced Airborne Research LiDAR (EAARL). The authors conclude that ALB can map channel topography with sufficient accuracy to support numerical flow models.…”

Taking the river inside: Fundamental advances from laboratory experiments in measuring and understanding bedload transport processes

“…The standard deviation and probability density function of bed elevations or one-and two-dimensional structure functions hold promise to better characterize bed roughness and structure (e.g., clusters of grains) (e.g., Marion et al, 2003). Green LiDAR, which has been used with success in the field to measure bed topography below water surfaces (McKean et al, 2014), has not been applied in laboratory flumes but might be applicable in future experiments. Sonar scans do not have the accuracy (often ≥ 1 mm) or resolution of laser scans (e.g., Singh et al, 2012;Venditti et al, 2012) but can measure bed topography during experiments when water is covering the bed.…”

Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: A review

“…Directing budgeting of mass redistribution provides constraints on the magnitude and spatial patterns of geomorphic and ecologic processes. Further, HRT provides a much more detailed and reliable boundary condition for eco-hydro-morphodynamic models, especially insofar as it allows direct coupling with the built environment 625(Priestnall et al, 2000) and explicit representation of surface roughness (typically dominated by vegetation) (McKean andRoering, 2004;Glenn et al, 2006;Cavalli et al, 2008;McKean et al, 2014), which, for example, vast improvement in flood inundation prediction(NRC, 2007).…”