Articles | Volume 5, issue 3
Earth Surf. Dynam., 5, 557–570, 2017
Earth Surf. Dynam., 5, 557–570, 2017

Research article 14 Sep 2017

Research article | 14 Sep 2017

Deriving principal channel metrics from bank and long-profile geometry with the R package cmgo

Antonius Golly and Jens M. Turowski Antonius Golly and Jens M. Turowski
  • German Research Centre for Geosciences (GFZ), Telegrafenberg, 14473 Potsdam, Germany

Abstract. Landscape patterns result from landscape forming processes. This link can be exploited in geomorphological research by reversely analyzing the geometrical content of landscapes to develop or confirm theories of the underlying processes. Since rivers represent a dominant control on landscape formation, there is a particular interest in examining channel metrics in a quantitative and objective manner. For example, river cross-section geometry is required to model local flow hydraulics, which in turn determine erosion and thus channel dynamics. Similarly, channel geometry is crucial for engineering purposes, water resource management, and ecological restoration efforts. These applications require a framework to capture and derive the data. In this paper we present an open-source software tool that performs the calculation of several channel metrics (length, slope, width, bank retreat, knickpoints, etc.) in an objective and reproducible way based on principal bank geometry that can be measured in the field or in a GIS. Furthermore, the software provides a framework to integrate spatial features, for example the abundance of species or the occurrence of knickpoints. The program is available at and is free to use, modify, and redistribute under the terms of the GNU General Public License version 3 as published by the Free Software Foundation.

Short summary
Researchers of fluvial geomorphology require reliable information on channel width and its change in space and time. For example, to study bank erosion rates we need the local position of channel banks before and after a high flood event. Although deriving these metrics seems simple, researchers often use manual or arbitrary approaches that are not objective and reproducible. Here, we present an open-source software tool cmgo (R package) that meets the requirements of academic research.