TY  - JOUR
A1  - Gailleton, Boris
A1  - Mudd, Simon M.
A1  - Clubb, Fiona J.
A1  - Peifer, Daniel
A1  - Hurst, Martin D.
T1  - A segmentation approach for the reproducible extraction and quantification of knickpoints from river long profiles
JF  - Earth surface dynamics
N2  - 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 chi 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 Quadrilatero Ferrifero 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.
Y1  - 2019
U6  - https://doi.org/10.5194/esurf-7-211-2019
SN  - 2196-6311
SN  - 2196-632X
VL  - 7
IS  - 1
SP  - 211
EP  - 230
PB  - Copernicus
CY  - Göttingen
ER  - 
TY  - JOUR
A1  - Clubb, Fiona J.
A1  - Bookhagen, Bodo
A1  - Rheinwalt, Aljoscha
T1  - Clustering river profiles to classify geomorphic domains
JF  - Journal of geophysical research : Earth surface
N2  - The structure and organization of river networks has been used for decades to investigate the influence of climate and tectonics on landscapes. The majority of these studies either analyze rivers in profile view by extracting channel steepness or calculate planform metrics such as drainage density. However, these techniques rely on the assumption of homogeneity: that intrinsic and external factors are spatially or temporally invariant over the measured profile. This assumption is violated for the majority of Earth's landscapes, where variations in uplift rate, rock strength, climate, and geomorphic process are almost ubiquitous. We propose a method for classifying river profiles to identify landscape regions with similar characteristics by adapting hierarchical clustering algorithms developed for time series data. We first test our clustering on two landscape evolution scenarios and find that we can successfully cluster regions with different erodibility and detect the transient response to sudden base level fall. We then test our method in two real landscapes: first in Bitterroot National Forest, Idaho, where we demonstrate that our method can detect transient incision waves and the topographic signature of fluvial and debris flow process regimes; and second, on Santa Cruz Island, California, where our technique identifies spatial patterns in lithology not detectable through normalized channel steepness analysis. By calculating channel steepness separately for each cluster, our method allows the extraction of more reliable steepness metrics than if calculated for the landscape as a whole. These examples demonstrate the method's ability to disentangle fluvial morphology in complex lithological and tectonic settings.
KW  - clustering
KW  - river networks
KW  - topographic analysis
KW  - landscape evolution modeling
Y1  - 2019
U6  - https://doi.org/10.1029/2019JF005025
SN  - 2169-9003
SN  - 2169-9011
VL  - 124
IS  - 6
SP  - 1417
EP  - 1439
PB  - American Geophysical Union
CY  - Hoboken
ER  -