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A segmentation approach for the reproducible extraction and quantification of knickpoints from river long profiles.

Gailleton, Boris and Mudd, Simon M. and Clubb, Fiona J. and Peifer, Daniel and Hurst, Martin D. (2019) 'A segmentation approach for the reproducible extraction and quantification of knickpoints from river long profiles.', Earth surface dynamics., 7 (1). pp. 211-230.


Changes in the steepness of river profiles or abrupt vertical steps (i.e. waterfalls) are thought to be indicative of changes in erosion rates, lithology or other factors that affect landscape evolution. These changes are referred to as knickpoints or knickzones and are pervasive in bedrock river systems. Such features are thought to reveal information about landscape evolution and patterns of erosion, and therefore their locations are often reported in the geomorphic literature. It is imperative that studies reporting knickpoints and knickzones use a reproducible method of quantifying their locations, as their number and spatial distribution play an important role in interpreting tectonically active landscapes. In this contribution we introduce a reproducible knickpoint and knickzone extraction algorithm that uses river profiles transformed by integrating drainage area along channel length (the so-called integral or χ method). The profile is then statistically segmented and the differing slopes and step changes in the elevations of these segments are used to identify knickpoints, knickzones and their relative magnitudes. The output locations of identified knickpoints and knickzones compare favourably with human mapping: we test the method on Santa Cruz Island, CA, using previously reported knickzones and also test the method against a new dataset from the Quadrilátero Ferrífero in Brazil. The algorithm allows for the extraction of varying knickpoint morphologies, including stepped, positive slope-break (concave upward) and negative slope-break knickpoints. We identify parameters that most affect the resulting knickpoint and knickzone locations and provide guidance for both usage and outputs of the method to produce reproducible knickpoint datasets.

Item Type:Article
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Publisher statement:© Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.
Date accepted:No date available
Date deposited:24 July 2019
Date of first online publication:2019
Date first made open access:24 July 2019

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