A self-adaptive two-parameter method for characterizing roughness of multi-scale subglacial topography

• Published in: Journal of Glaciology
• Main Authors: Shinan Lang, Ben Xu, Xiangbin Cui, Kun Luo, Jingxue Guo, Xueyuan Tang, Yiheng Cai, Bo Sun, Martin J. Siegert
• Format: Article in Journal/Newspaper
• Language: English
• Published: Cambridge University Press 2021
• Subjects: Antarctic glaciology; glaciological instruments and methods; radio-echo sounding; Environmental sciences; GE1-350; Meteorology. Climatology; QC851-999; Antarctic; Antarc*; Journal of Glaciology
• Online Access: https://doi.org/10.1017/jog.2021.12; https://doaj.org/article/41bebf03000244df9332dd2fa582d282

During the last few decades, bed-elevation profiles from radar sounders have been used to quantify bed roughness. Various methods have been employed, such as the ‘two-parameter’ technique that considers vertical and slope irregularities in topography, but they struggle to incorporate roughness at multiple spatial scales leading to a breakdown in their depiction of bed roughness where the relief is most complex. In this article, we describe a new algorithm, analogous to wavelet transformations, to quantify the bed roughness at multiple scales. The ‘Self-Adaptive Two-Parameter’ system calculates the roughness of a bed profile using a frequency-domain method, allowing the extraction of three characteristic factors: (1) slope, (2) skewness and (3) coefficient of variation. The multi-scale roughness is derived by weighted-summing of these frequency-related factors. We use idealized bed elevations to initially validate the algorithm, and then actual bed-elevation data are used to compare the new roughness index with other methods. We show the new technique is an effective tool for quantifying bed roughness from radar data, paving the way for improved continental-wide depictions of bed roughness and incorporation of this information into ice flow models.