An automated knickzone selection algorithm (KZ‐Picker) to analyze transient landscapes: Calibration and validation

Abstract

Streams commonly respond to base‐level fall by localizing erosion within steepened, convex knickzone reaches. Localized incision causes knickzone reaches to migrate upstream. Such migrating knickzones dictate the pace of landscape response to changes in tectonics or erosional efficiency and can help quantify the timing and source of base‐level fall. Identification of knickzones typically requires individual selection of steepened reaches: a process that is tedious and subjective and has no efficient means to measure knickzone size. We construct an algorithm to automate this procedure by selecting the bounds of knickzone reaches in a χ‐space (drainage‐area normalized) framework. An automated feature calibrates algorithm parameters to a subset of knickzones handpicked by the user. The algorithm uses these parameters as consistent criteria to identify knickzones objectively, and then the algorithm measures the height, length, and slope of each knickzone reach. We test the algorithm on 1, 10, and 30 m resolution digital elevation models (DEMs) of six catchments (trunk‐stream lengths: 2.1–5.4 km) on Santa Cruz Island, southern California. On the 1 m DEM, algorithm‐selected knickzones confirm 93% of handpicked knickzone positions (n = 178) to a spatial accuracy of ≤100 m, 88% to an accuracy within 50 m, and 46% to an accuracy within 10 m. Using 10 and 30 m DEMs, accuracy is similar: 88–86% to ≤100 m and 82% to ≤50 m (n = 38 and 36, respectively). The algorithm enables efficient regional comparison of the size and location of knickzones with geologic structures, mapped landforms, and hillslope morphology, thereby facilitating approaches to characterize the dynamics of transient landscapes.

Clustering River Profiles to Classify Geomorphic Domains

“…For example, in the Pozo catchment, the results of the clustering were primarily correlated to lithological variations between a weaker, unconsolidated shale unit compared to more resistant volcaniclastics. This lithological impact on the river profiles persists despite evidence for propagation of transient signals from sea level changes through the catchment, such as the preservation of knickpoints, hanging valleys, and marine terraces (Neely et al, ), as well as recent anthropogenic erosion (Perroy, , ). Although we perform the clustering based on the channel profiles, our analysis need not be restricted to purely river profile analysis: we also extracted the catchments associated with each cluster, allowing us to compare landscape relief and gradient across each cluster, as shown in Figures and .…”

“…For example, in the Pozo catchment, the results of the clustering were primarily correlated to lithological variations between a weaker, unconsolidated shale unit compared to more resistant volcaniclastics. This lithological impact on the river profiles persists despite evidence for propagation of transient signals from sea level changes through the catchment, such as the preservation of knickpoints, hanging valleys, and marine terraces (Neely et al, 2017), as well as recent anthropogenic erosion (Perroy, 2009(Perroy, , 2010. Although…”

“…For many decades, the study of river networks has been a core concept in geomorphic theory and research. Both the planforms and profiles of fluvial channels have been used to answer diverse problems, such as constraining changes in uplift rates (e.g., Kirby et al, 2003;Kirby & Whipple, 2001;Lavé & Avouac, 2001;Nennewitz et al, 2018), deducing throw rates from faulting (e.g., Whittaker et al, 2008), isolating patterns of drainage capture (e.g., Giachetta & Willett, 2018;Willett et al, 2014), detecting signatures of climate (e.g., Hobley et al, 2012;Roe et al, 2002;Ranjbar et al, 2018;Seybold et al, 2017), and quantifying the impact of different erosional processes on drainage networks (e.g., Bookhagen & Strecker, 2012;Clubb et al, 2016;DiBiase et al, 2010;Hooshyar et al, 2017;Neely et al, 2017;Olen et al, 2016;Stock & Dietrich, 2003).…”

Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques

“…Primarily, in studies of river profiles one may wish to remove noise from profiles while preserving underlying patterns and quantifying uncertainty. In tectonic geomorphology, for example, knickzones in longitudinal river profiles are essential proxies for transient river adjustment (Bishop et al, 2005), but distinguishing actual knickzones from data artifacts is challenging (Neely et al, 2017). Interquantile ranges determined by the CRS algorithm provide an objective way to determine minimum elevation drops that a knickzone must have to be identified against the background noise.…”

Origin of Knickpoints in an Alpine Context Subject to Different Perturbing Factors, Stura Valley, Maritime Alps (North-Western Italy)

“…While the former usually inherit information on tectonic uplift or climatic changes, the latter may be related to local fluvial captures, lithological control, and landslides. The detection, dynamics, and influence of the knickpoints in landscape evolution have been investigated in recent studies [17][18][19][20][21], which emphasize that mono-causal knickpoints in river networks are generally well understood, but that in the majority of natural catchments multi-causal knickzone systems are possible.The aim of this study is to identify the prevailing processes active in generating knickpoints in such a natural, alpine basin, which is affected by tectonic uplift and Pleistocene glaciation and is sensitive to base-level changes at the transition between mountain front and foreland. We chose the Stura River basin (Figure 1) because its morphology was shaped by tectonics, Quaternary glacial phases, variable lithology, and base-level falls created by river captures.…”