Articles | Volume 70, issue 4
https://doi.org/10.5194/gh-70-265-2015
https://doi.org/10.5194/gh-70-265-2015
Standard article
 | 
06 Oct 2015
Standard article |  | 06 Oct 2015

Methods for detecting channel bed surface changes in a mountain torrent – experiences from the Dorfbach torrent

C. Willi, C. Graf, Y. Deubelbeiss, and M. Keiler

Abstract. The erosion of and depositions on channel bed surfaces are instrumental to understanding debris flow processes. We present an overview of existing field methods and highlight their respective advantages and disadvantages. Terrestrial laser scanning (TLS), airborne laser scanning (ALS), erosion sensors, cross sections (CS) and geomorphological mapping are compared. Additionally, two of these approaches (i.e. TLS and CS) are tested and applied in the channel reaches of the torrent catchments. The results of the comparison indicate that the methods are associated with variable temporal and spatial resolution as well as data quality and invested effort. TLS data were able to quantify small-scale variations of erosion and deposition volumes. While the same changes could be detected with CS and geomorphological mapping, it was only possible with lower precision and coarser spatial resolution. The study presents a range of potential methods that can be applied accordingly to address the objectives and to support the analyses of specific applications. The availability of erosion data, acquired mainly by TLS and ALS, in combination with debris-flow monitoring data, provides promising sources of information to further support torrent risk management.

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Short summary
The erosion of and depositions on channel bed surfaces are instrumental to understanding debris flow processes. We present different methods and highlight their pro and cons. Terrestrial and airborne laser scanning, erosion sensors, cross sections and geomorphological mapping are compared. Two of these approaches are tested and applied in a torrent. The results indicate that the methods are associated with variable temporal and spatial resolution as well as data quality and invested effort.