Preprints
https://doi.org/10.5194/tc-2021-2
https://doi.org/10.5194/tc-2021-2

  29 Mar 2021

29 Mar 2021

Review status: this preprint is currently under review for the journal TC.

Ice Roughness Estimation via Remotely Piloted Aircraft and Photogrammetry

James Ehrman, Shawn Clark, and Alexander Wall James Ehrman et al.
  • Department of Civil Engineering, 15 Gillson St., University of Manitoba, Winnipeg, MB R3T 5V6 Canada

Abstract. Structure-from-Motion Photogrammetry conducted with images obtained via Remotely Piloted Aircraft (RPA) has revolutionized the field of land surface monitoring. RPA-Photogrammetry can quickly and easily capture a full 3D representation of a study area. The result of this process is a high-definition Digital Elevation Model (DEM) representing the land surface of a given study area. It is particularly useful in applications where land surface data collection would otherwise be expensive or dangerous. The monitoring of fluvial ice covers can be time-intensive, dangerous, and costly, if detailed data are required. Fluvial ice roughness is a sensitive parameter in hydraulic models and is incredibly difficult to measure directly using traditional field methods. This research hypothesized that the surface roughness of a newly-frozen fluvial ice cover is indicative of subsurface roughness. The hypothesis was tested through a comparison of ice roughness determined through the statistical analysis of RPA-photogrammetry DEMs to ice roughness values predicted by the Nezhikhovskiy equation. The Nezhikhovskiy equation is a widely used empirical method for estimating ice roughness based on observed ice thickness. Hydraulic and topographic data were collected over two years of field research on the Dauphin River in Manitoba, Canada. Various statistical metrics were used to represent the roughness of the DEMs. Strong trends were identified in the comparison of ice cover roughness values determined through RPA-photogrammetry and those calculated via the Nezhikhovskiy equation, as well as with ice thickness. The inter-quartile range of observed roughness heights was determined to be the most representative roughness metric. The maximum peak value performed better in some cases, but the fact that this metric would be heavily influenced by outliers led to it being rejected as a representative metric. Three distinct forms of surface ice roughness were noted: rough, smooth, and ridged. Statistical properties of the DEMs of fluvial ice covers were calculated. No DEMs were found to be normally distributed. k-means clustering analysis was used to group sampled data into two categories, which were interpreted as rough and smooth ice. The inter-quartile range of the smooth and rough categories were found to be 0.01–0.05 meters and 0.07–0.12 meters, respectively. RPA-photogrammetry was concluded to be a suitable method for the monitoring of fluvial ice covers. Other applications of RPA-photogrammetry for the characterization of fluvial ice covers are proposed.

James Ehrman et al.

Status: open (until 27 May 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

James Ehrman et al.

James Ehrman et al.

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Short summary
This research proposes and tests new methods for the estimation of the surface roughness of newly-formed river ice covers. The hypothesis sought to determine if surface ice roughness was indicative of the subsurface. Ice roughness has consequences for winter flow characteristics of rivers, and can greatly impact river ice jams. Remotely piloted aircraft and photogrammetry were used, and good correlation was found between the observed surface ice roughness and estimated subsurface ice roughness.