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**Research article**
22 Jun 2018

**Research article** | 22 Jun 2018

Channel concavity methods

^{1}School of GeoSciences, University of Edinburgh, Drummond Street, Edinburgh EH8 9XP, UK^{2}Institute of Earth and Environmental Science, University of Potsdam, 14476 Potsdam, Germany^{3}School of Geographical and Earth Sciences, University of Glasgow, University Avenue, Glasgow G12 8QQ, UK

Abstract

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For over a century, geomorphologists have attempted to unravel information
about landscape evolution, and processes that drive it, using river profiles.
Many studies have combined new topographic datasets with theoretical models
of channel incision to infer erosion rates, identify rock types with
different resistance to erosion, and detect potential regions of tectonic
activity. The most common metric used to analyse river profile geometry is
channel steepness, or *k*_{s}. However, the calculation of channel
steepness requires the normalisation of channel gradient by drainage area.
This normalisation requires a power law exponent that is referred to as the
channel concavity index. Despite the concavity index being crucial in
determining channel steepness, it is challenging to constrain. In this
contribution, we compare both slope–area methods for calculating the
concavity index and methods based on integrating drainage area along the
length of the channel, using so-called “chi” (*χ*) analysis. We present
a new *χ*-based method which directly compares *χ* values of tributary
nodes to those on the main stem; this method allows us to constrain the
concavity index in transient landscapes without assuming a linear
relationship between *χ* and elevation. Patterns of the concavity index
have been linked to the ratio of the area and slope exponents of the stream
power incision model (*m*∕*n*); we therefore construct simple numerical models
obeying detachment-limited stream power and test the different methods
against simulations with imposed *m* and *n*. We find that *χ*-based
methods are better than slope–area methods at reproducing imposed *m*∕*n*
ratios when our numerical landscapes are subject to either transient uplift
or spatially varying uplift and fluvial erodibility. We also test our methods
on several real landscapes, including sites with both lithological and
structural heterogeneity, to provide examples of the methods' performance and
limitations. These methods are made available in a new software package so
that other workers can explore how the concavity index varies
across diverse landscapes, with the aim to improve our understanding of the
physics behind bedrock channel incision.

How to cite

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How to cite.

Mudd, S. M., Clubb, F. J., Gailleton, B., and Hurst, M. D.: How concave are river channels?, Earth Surf. Dynam., 6, 505-523, https://doi.org/10.5194/esurf-6-505-2018, 2018.

1 Introduction

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Geomorphologists have been interested in understanding controls on the steepness of river channels for centuries. In his seminal “Report on the Henry Mountains”, Gilbert (1877) remarked that “We have already seen that erosion is favoured by declivity. Where the declivity is great the agents of erosion are powerful; where it is small they are weak; where there is no declivity they are powerless” (p. 114). Following Gilbert's pioneering observations of landscape form, many authors have attempted to quantify how topographic gradients (or declivities) relate to erosion rates. Landscape erosion rates are thought to respond to tectonic uplift (Hack, 1960). Therefore, extracting erosion rate proxies from topographic data provides novel opportunities for identifying regions of tectonic activity (Cyr et al., 2010; Lague and Davy, 2003; Seeber and Gornitz, 1983; Snyder et al., 2000; Wobus et al., 2006a), and may even be able to highlight potentially active faults (Kirby and Whipple, 2012). Analysing channel networks is particularly important for detecting the signature of external forcings from the shape of the topography, as fluvial networks set the boundary conditions for their adjacent hillslopes, therefore acting as the mechanism by which climatic and tectonic signals are transmitted to the rest of the landscape (Burbank et al., 1996; Hurst et al., 2013; Whipple, 2004; Whipple and Tucker, 1999).

Channels do not yield such information easily, however. Any observer of rivers or mountains will note that headwater channels tend to be steeper than channels downstream. Declining gradients along the length of the channel lead to river longitudinal profiles that tend to be concave up. Therefore, the gradient of a channel cannot be related to erosion rates in isolation; some normalising procedure must be performed. Over a century ago, Shaler (1899) postulated that as channels gain drainage area their slopes would decline, hindering their ability to erode. Authors such as Hack (1957), Morisawa (1962), and Flint (1974) expanded upon this idea in the early 20th century by quantifying the relationship between slope and drainage area, often used as a proxy for discharge. Flint (1974) found that channel gradient appeared to systematically decline downstream in a trend that could be described by a power law:

$$\begin{array}{}\text{(1)}& S={k}_{\mathrm{s}}{A}^{-\mathit{\theta}},\end{array}$$

where *θ* is referred to as the concavity index since it describes how
concave a profile is; the higher the value, the more rapidly a channel's
gradient decreases downstream. Note that the term “concavity” is sometimes
applied to river long profiles (Conway et al., 2015; Demoulin, 1998; Roy and Sinha, 2018), so most studies refer to
*θ*, derived from slope–area data, as the “concavity index”
(Kirby and Whipple, 2012). The
term *k*_{s} is called the steepness index, as it sets the overall
gradient of the channel. If we take the logarithm of both sides of
Eq. (1), we find a linear relationship in
log[*S*]–log[*A*] space with a slope of *θ* and an intercept (the value
of log[*S*], where log[*A*] = 0) of log[*k*_{s}]:

$$\begin{array}{}\text{(2)}& \mathrm{log}\left[S\right]=-\mathit{\theta}\mathrm{log}\left[A\right]+\mathrm{log}\left[{k}_{\mathrm{s}}\right].\end{array}$$

A number of studies
(DiBiase et al., 2010; Harel et al., 2016; Mandal et al., 2015; Ouimet et al., 2009; Scherler et al., 2014) have demonstrated that *k*_{s}
is positively correlated with erosion rate, mirroring the predictions of
Gilbert (1877) over a century earlier. Many authors have used
channel steepness to examine fluvial response to climate, lithology, and
tectonics
(Cyr et al., 2010; DiBiase et al., 2010; Flint, 1974; Harkins et al., 2007; Kirby and Whipple, 2001, 2012; Kobor and Roering, 2004; Lague and Davy, 2003; Snyder et al., 2000; Tarboton et al., 1989; Vanacker et al., 2015; Wobus et al., 2006a).

The noise inherent in *S*–*A* analysis prompted Leigh Royden and colleagues
to develop a method that compares the elevations of channel profiles, rather
than slope (Royden et al., 2000). We can modify the
Royden et al. (2000) approach to integrate
Eq. (1), since *S* = d*z*∕d*x* where
*z* is elevation and *x* is distance along the channel
(Whipple et al., 2017), resulting in

$$\begin{array}{}\text{(3)}& z\left(x\right)=z\left({x}_{b}\right)+\left({\displaystyle \frac{{k}_{\mathrm{s}}}{{{A}_{\mathrm{0}}}^{\mathit{\theta}}}}\right)\underset{{x}_{b}}{\overset{x}{\int}}({\displaystyle \frac{{A}_{\mathrm{0}}}{A\left(x\right)}}{)}^{\mathit{\theta}}\mathrm{d}x,\end{array}$$

where *A*_{0} is a reference drainage area, introduced to non-dimensionalise the
area term within the integral in Eq. (3).
We can then define a longitudinal coordinate, *χ*:

$$\begin{array}{}\text{(4)}& \mathit{\chi}=\underset{{x}_{b}}{\overset{x}{\int}}({\displaystyle \frac{{A}_{\mathrm{0}}}{A\left(x\right)}}{)}^{\mathit{\theta}}\mathrm{d}x.\end{array}$$

*χ* has dimensions of length and is defined such that at any point in the
channel

$$\begin{array}{}\text{(5)}& z\left(x\right)=z\left({x}_{b}\right)+\left({\displaystyle \frac{{k}_{\mathrm{s}}}{{{A}_{\mathrm{0}}}^{\mathit{\theta}}}}\right)\mathit{\chi}.\end{array}$$

Equation (5) shows that steepness of the channel
(*k*_{s}) is related to the slope of the transformed channel in
*χ*–elevation space. In both Eqs. (2) and
(5), the numerical value of *k*_{s} depends on the
value chosen for the concavity index, *θ*. In order to compare the
steepness of channels in basins of different sizes, a reference concavity
index is typically chosen (*θ*_{ref}), which is then used to
extract a normalised channel steepness from the data
(Wobus et al., 2006a):

$$\begin{array}{}\text{(6)}& {k}_{\mathrm{sn},i}={A}_{i}^{{\mathit{\theta}}_{\mathrm{ref}}}{S}_{i},\end{array}$$

where the subscript *i* is a data point. This data point often represents
channel quantities that are smoothed; see discussion below.

The choice of the reference concavity index is important in determining the
relative *k*_{sn} values amongst different sections in the channel
network, which we illustrate in Fig. 1. This
figure depicts hypothetical slope–area data, which appear to lie along a
linear trend in slope–area space. Choosing a reference concavity index based
on a regression through these data will result in the entire channel network
having similar values of *k*_{sn}. Based on the data in
Fig. 1, there is no evidence that the correct
concavity index is anything other than the one represented by the linear fit
through the data. However, these hypothetical data are in fact based on
numerical simulations, presented in Sect. 3, in which we
simulated a higher uplift rate in the core of the mountain range. The correct
concavity index is therefore lower than that indicated by the
log[*S*]–log[*A*] data, and instead the data show a strong spatial trend in
channel steepness (interpretation 2 in Fig. 1).
The simplest interpretation based on log[*S*]–log[*A*] data alone would have
been entirely incorrect. This situation is analogous to the one described by
Kirby and Whipple (2001), where downstream reductions in uplift rates
in the Siwalik Hills of India and Nepal resulted in elevated apparent
concavities. Conversely, if a single reference concavity index is chosen in
an area with changing concavity indices, then spurious patterns in
*k*_{sn} may arise (Gasparini and Whipple, 2014). These
examples highlight that selecting the correct concavity index is crucial if
we are to correctly interpret channel steepness data.

Extracting a reliable reference concavity indices from slope–area data on
real landscapes is challenging: topographic data can be noisy, leading to a
wide range of channel gradients for small changes in drainage area. The
branching nature of river networks also results in large discontinuities in
drainage areas where tributaries meet, resulting in significant data gaps in
*S*–*A* space (Fig. 2). Wobus et al. (2006a) made
recommendations for preprocessing of slope–area data that are still used in
many studies. First, the digital elevation model (DEM) is smoothed (although with improved DEMs this is
now rare), then topographic gradient is measured over either a fixed reach
length or a fixed drop in elevation (Wobus et al., 2006a,
recommends the latter), and then the data are averaged in logarithmically
spaced bins. More recently, authors have proposed alternative channel
smoothing strategies
(Aiken and Brierley, 2013; Schwanghart and Scherler, 2017); all these
methods use some form of smoothing and averaging.

In order to circumvent these problems with *S*–*A* analysis, many authors
have since used the integral approach (Eq. 5)
to analyse channel concavity indices. This method also aims to normalise
river profiles for their drainage area, but rather than comparing slope to
area, it integrates area along channel length (Perron and Royden, 2013; Royden et al., 2000). Perron and Royden (2013) showed that the concavity
index could be extracted from a channel by selecting the value of
*θ*_{ref} that results in the most linear channel profile in
*χ*–elevation space. However, if there are sections of the channel with
different *k*_{sn} values, this will hinder our ability to extract
the *θ*_{ref} value, as it is not appropriate to fit a single
line throughout the entire profile (Mudd et al., 2014).
Mudd et al. (2014) introduced a method to statistically determine
the most likely concavity index by computing the best-fit series of linear
segments using an algorithm that balanced fit of the data against
overparameterisation using the Akaike information criterion
(Akaike, 1974). This method, however, requires a number of input
parameters and also performs computationally expensive segmentation on the
*χ*–elevation data prior to calculating the concavity index
(Mudd et al., 2014).

Perron and Royden (2013) suggested a second independent means to
calculate the concavity index, which does not assume linearity of the
profiles in *χ*–elevation space and may therefore be used in transient
landscapes. This method is instead based on searching for collinearity of
tributaries with the main stem channel
(Mudd et al., 2014; Perron and Royden, 2013), and has since
been used as a basis for other techniques that aim to minimise some
quantitative description of scatter between tributaries and the trunk channel
Goren et al. (2014); Hergarten et al. (2016).

Although the collinearity test does not assume any linearity of profiles in
*χ*–elevation space, it does rely on the assumption that points in the
channel network with the same value of *χ* will have the same elevation.
Perron and Royden (2013) noted that this will hold true if transient
erosion signals propagate vertically through the network at a constant rate,
which has been predicted by some theoretical models of fluvial incision
(Wobus et al., 2006b). Royden and Perron (2013) went on to
demonstrate that changes in erosion rates in channel networks would lead to
distinct segments that migrate upstream, which they termed “slope patches”,
where the local *k*_{s} reflects local erosion rate. However, here, we
wish to avoid basing our formulation on any theoretical models of fluvial
incision, as this introduces assumptions regarding bedrock erosion processes.
Y. Wang et al. (2017) highlighted that slope–area analysis could be
used to complement integral analysis where concavity indices might vary
spatially, since slope–area is agnostic with regard to these incision
processes, but also found that integral-based analyses have lower
uncertainties than slope–area analysis. Therefore, here, we use simple
geometric relationships of knickpoint propagation to relate the collinearity
test to channel concavity indices without relying on theoretical models of
fluvial incision, as set out in Sect. 1.2.

Playfair (1802) noted that tributary valleys tended to
join the principal valley at a common elevation, suggesting that, at their
outlets, the tributary streams must lower at the same rate as the principal
streams into which they drain. Therefore, any change in incision rate on the
main stem channel will be transmitted to the upstream tributaries. Using
simple geometric relationships, Niemann et al. (2001) showed that
a knickpoint should migrate upstream with a horizontal celerity
(Ce_{h}, in length per time) of

$$\begin{array}{}\text{(7)}& {\mathrm{Ce}}_{\mathrm{h}}={\displaystyle \frac{\mathrm{1}}{{S}_{\mathrm{2}}-{S}_{\mathrm{1}}}}\mathrm{\Delta}E,\end{array}$$

where *S*_{1} is the channel slope prior to disturbance, *S*_{2} is the channel
slope after disturbance (e.g. due to a change in incision rate *E*), and
Δ*E* is the difference between the incision rate before and after
disturbance (which can be equated to uplift rates *U*_{1} and *U*_{2} in units of
length per time, $\mathrm{\Delta}E={U}_{\mathrm{2}}-{U}_{\mathrm{1}}$). Wobus et al. (2006b) simply
inserted Eq. (1) into Eq. (7) so that the
horizontal celerity is simply a function of drainage area, assuming that the
concavity index is independent of rock uplift rate:

$$\begin{array}{}\text{(8)}& {\mathrm{Ce}}_{\mathrm{h}}={\displaystyle \frac{{U}_{\mathrm{2}}-{U}_{\mathrm{1}}}{{k}_{\mathrm{s}\mathrm{2}}-{k}_{\mathrm{s}\mathrm{1}}}}{A}^{\mathit{\theta}}.\end{array}$$

Noting that vertical celerity is simply the horizontal celerity multiplied by
the local slope after disturbance *S*_{2}, Wobus et al. (2006b) showed
that the vertical celerity (Ce_{v}) was not a function of
drainage area:

$$\begin{array}{}\text{(9)}& {\mathrm{Ce}}_{\mathrm{v}}={\displaystyle \frac{{U}_{\mathrm{2}}-{U}_{\mathrm{1}}}{{k}_{\mathrm{s}\mathrm{2}}-{k}_{\mathrm{s}\mathrm{1}}}}{k}_{\mathrm{s}\mathrm{2}}.\end{array}$$

Thus, if we assume spatially homogeneous uplift and constant erodibility (i.e. channels with the same slope and drainage area erode at the same rate), then the vertical celerity propagating up the principal stream and all tributaries will be a constant. Equation (9) is derived from purely geometric relationships, suggesting that collinearity can be used to estimate the concavity index without assuming any theoretical models of fluvial incision under the assumption that the incision process will result in local slope–area relationships reflecting Eq. (1).

2 Calculation of concavity indices using collinearity

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In this study, we revisit commonly used methods for estimating the concavity index using both slope–area analysis and collinearity methods based on integral analysis. Our objective is to determine the strengths and weaknesses of established methods alongside several new methods developed for this study, as well as to quantify the uncertainties in the concavity index. We present these methods in an open-source software package that can be used to constrain concavity indices across multiple landscapes. This information may give insight into the physical processes responsible for channel incision into bedrock, which are as yet poorly understood.

For slope–area analysis, in this paper, we forgo initial smoothing of the DEM
and use a fixed elevation drop along a D8 drainage pathway implemented using
the network extraction algorithm of Braun and Willett (2013). We calculate the
best-fit concavity indices using two different methods: (i) concavity indices
extracted from all slope–area (*S*–*A*) data (i.e. no logarithmic bins,
every tributary) and (ii) concavity indices of contiguous channel profile
segments with consistent *S*–*A* scaling within the log-binned *S*–*A* data
of the trunk stream, calculated using the statistical segmentation algorithm
described in Mudd et al. (2014). We report the different extracted
concavity indices and their uncertainties in the results below.

Here, we present two new methods of identifying collinear tributaries in
*χ*–elevation space in order to constrain the best-fit concavity indices
from fluvial profiles. Rather than fitting segments to the profiles, which is
computationally expensive, we directly compare all the elevation data of the
tributaries in each drainage basin to the main stem. This is not completely
straightforward, however: because the *χ* coordinate integrates area and
channel distance, it is very unlikely that a pixel on a tributary channel
shares a *χ* coordinate with any pixel on the main stem. Instead, for
every tributary pixel, we compare the tributary elevation with an elevation on
the main stem at the same *χ* computed with a linear fit between the two
pixels with the nearest *χ* coordinates
(Fig. 3). We then calculate a maximum
likelihood estimator (MLE) for each tributary. The MLE is calculated with

$$\begin{array}{}\text{(10)}& \mathrm{MLE}=\prod _{i=\mathrm{1}}^{N}\mathrm{exp}[-{\displaystyle \frac{{r}_{i}^{\mathrm{2}}}{\mathrm{2}{\mathit{\sigma}}^{\mathrm{2}}}}],\end{array}$$

where *N* is the number of nodes in the tributary, *r*_{i} is the calculated
residual between the elevation of tributary node *i* and the linear
regression of elevation on the main stem, and *σ* is a scaling factor. If
*r*_{i} is 0 for all nodes, then MLE = 1 (i.e. MLE varies between 0 and
1, with 1 being the maximum possible likelihood).

For a given drainage basin, we can multiply the MLE for each tributary to get
the total MLE for the basin, and we can do this for a range of concavity
indices to calculate the most likely value of *θ*. Because
Eq. (10) is a product of negative exponentials, the value of the
MLE will decrease as *N* increases, and in large datasets this results in MLE
values below the smallest number that can be computed, meaning that in large
datasets MLE values can often be reported as zero. To counter this effect, we
increase *σ* until all tributaries have non-zero MLE values. As *σ*
is simply a scaling factor, this does not affect which concavity index is
calculated as the most likely value once all tributaries have non-zero MLEs
(see the Supplement).

There are two disadvantages to using Eq. (10) on all points in the
channel network. Firstly, because the MLE is calculated as a product of
exponential functions, each data point will reduce the MLE, and so tributaries
will influence MLE in proportion to their length. Secondly, because we use
all data, we cannot estimate uncertainty when computing the most likely
concavity index. Therefore, we apply a second method to the *χ*–elevation
data that mitigates these two shortcomings by bootstrap sampling the data.
This method evaluates a fixed number of discrete points on each tributary,
but the points are selected randomly and this random selection is done
iteratively, building up a population of MLE values for each concavity index.

For each iteration of the bootstrap method, we create a template of points in
*χ* space, measured from the confluence of each tributary from the trunk
channel (Fig. 4). We start by selecting a maximum
value of *χ* upstream of the tributary junction, and then separate this
space into *N*_{BS} nodes. We create evenly spaced bins between the
maximum value of *χ* in the template, and then in each iteration randomly
select one point in each bin. Using this template on each tributary, we
calculate the residuals between the tributary and the trunk channel using
Eq. (10). If, for a given tributary, a point in the template is
located beyond the end of the tributary, then the point is excluded from the
calculation of MLE. Figure 4 provides a schematic
visualisation of this method.

We repeat these calculations over many iterations, and for each concavity index we compute the median MLE, the minimum and maximum MLEs, and the first and third quartile MLEs. We approximate the uncertainty range by first taking the most likely concavity index (having highest median MLE value amongst all concavity indices tested). We then find the span of concavity indices whose third quartile MLE values exceed the first quartile MLE value of the most likely concavity indices (Fig. 4).

One complication of using collinearity to calculate the most likely concavity
index is that occasionally one may find a hanging tributary
(Crosby et al., 2007; Wobus et al., 2006b), which could occur
for a variety of reasons, such as the presence of geologic structures or
lithologic variability. A hanging tributary can skew the overall MLE values
in a basin, so in each basin we test the MLE and RMSE values in each
tributary for outliers and iteratively remove these outlying tributaries,
testing for the most likely concavity index on each iteration. However, we
find that eliminating outlying tributaries has a minimal effect on the
calculated concavity index. The other primary complication is that one must
assume a concavity index prior to performing the *χ* transformation
(Eq. 4), and thus slope–area analysis may be more suited to
detecting changes in concavity within basins
(Y. Wang et al., 2017). We suggest here an alternative approach
of calculating concavity using *χ* methods in many small basins to look
for any systematic changes. Before we can perform such analyses, however, we
must constrain our confidence in estimates of the concavity index.

We also implement a disorder statistic (Goren et al., 2014; Hergarten et al., 2016; Shelef et al., 2018) that aims to quantify
differences in the *χ*–elevation patterns between tributaries and the
trunk channel. Here, we follow the method of Hergarten et al. (2016).
The disorder statistic is calculated by first taking the *χ*–elevation
pairs of every point in the channel network, ordered by increasing elevation.
We calculate the sum:

$$\begin{array}{}\text{(11)}& S=\sum _{i=\mathrm{1}}^{N}|{\mathit{\chi}}_{\mathrm{s},\phantom{\rule{0.125em}{0ex}}i+\mathrm{1}}-{\mathit{\chi}}_{\mathrm{s},\phantom{\rule{0.125em}{0ex}}i}|,\end{array}$$

where the subscript s, *i* represents the *i*th *χ* coordinate that has
been sorted by its elevation. The sum, *S*, is minimal if elevation and *χ*
are related monotonically. However, it scales with the absolute values of
*χ*, which are sensitive to the concavity index (see
Eq. 4), so following Hergarten et al. (2016), we
scale the disorder metric, *D*, by the maximum value of *χ* in the
tributary network (*χ*_{max}):

$$\begin{array}{}\text{(12)}& D={\displaystyle \frac{\mathrm{1}}{{\mathit{\chi}}_{max}}}\left(\sum _{i=\mathrm{1}}^{N}\right|{\mathit{\chi}}_{\mathrm{s},\phantom{\rule{0.125em}{0ex}}i+\mathrm{1}}-{\mathit{\chi}}_{\mathrm{s},\phantom{\rule{0.125em}{0ex}}i}|-{\mathit{\chi}}_{max}).\end{array}$$

The disorder metric relies on the use of all the data in a tributary network,
meaning that only one value of *D* can be calculated for each basin.
Therefore, we cannot estimate the uncertainty in concavity index using this
statistic alone. Furthermore, the random sampling approach we take with the
previous *χ* methods is not appropriate, as skipping nodes in the
*χ*–elevation sequence will lead to large values of *S* and substantially
increase the disorder metric. We therefore employ a bootstrap approach based
on the analysis of entire tributaries within each basin. First, we find every
combination of three tributaries plus the trunk stream in the basin. For each
combination, we then iterate through a range of concavity indices and
calculate the disorder metric. This allows us to find the concavity index
that minimises the disorder metric for each combination, resulting in a
population of best-fit concavities, from which we calculate the median and
interquartile range.

3 Testing on numerical landscapes

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In real landscapes, we can only approximate the concavity index based on topography. However, we can create simulations where we fix a known concavity index and see if our methods reproduce this value. To do this, we rely on simple simulations driven by the general form of the stream power incision model, first proposed by Howard and Kerby (1983):

$$\begin{array}{}\text{(13)}& E=K{A}^{m}{S}^{n},\end{array}$$

where *E* is the long-term fluvial incision rate, *A* is the upstream
drainage area, *S* is the channel gradient, *K* is the erodibility
coefficient, which is a measure of the efficiency of the incision process,
and *m* and *n* are exponents. A number of variations of this equation are
possible: some authors have proposed, for example, modifications that involve
erosion thresholds (Tucker and Bras, 2000) or modulation by
sediment fluxes (Sklar and Dietrich, 1998). However,
Gasparini and Brandon (2011) showed that many of the modified versions
of Eq. (13) could be captured simply by modifying the exponents
*m* and *n*.

We have chosen this model because it can be related to the concavity index
and therefore can be used to test the different methods under idealised
conditions. We can relate the stream power incision model to
Eq. (1) by rearranging Eq. (13) to solve for
channel slope and relating it to local erosion rate, *E*:

$$\begin{array}{}\text{(14)}& S=({\displaystyle \frac{E}{K}}{)}^{\mathrm{1}/n}{A}^{-m/n}.\end{array}$$

Comparing Eqs. (1) and (14)
reveals that the ratio between area and slope exponents in the stream power
incision model, *m*∕*n*, is therefore equivalent to *θ* from
Eq. (1). The channel steepness index, *k*_{s}, is
related to erosion rate by

$$\begin{array}{}\text{(15)}& {k}_{\mathrm{s}}=({\displaystyle \frac{E}{K}}{)}^{\mathrm{1}/n}.\end{array}$$

The stream power incision model also makes predictions about how tectonic
uplift can be translated into local erosion rates
(Whipple and Tucker, 1999), and the predicted relationship
between the channel steepness index and uplift has been exploited by a number
of studies to identify areas of tectonic activity
(Kirby and Whipple, 2012; Kirby et al., 2003; Wobus et al., 2006a).
Furthermore, many workers have used the framework of the stream power
incision model to extract uplift histories
(Fox et al., 2014; Goren et al., 2014; Pritchard et al., 2009; Roberts and White, 2010).
However, the ability of these studies to extract information from channel
profiles is dependent on the both the *m*∕*n* ratio, equivalent to *θ*,
and the slope exponent, *n*, which are key unknowns within these theoretical
models of fluvial incision. The *m*∕*n* ratio is frequently assumed to be equal
to 0.5, with *n* assumed to be unity, despite recent compilations of data
from multiple landscapes showing that this may not be the case
(Clubb et al., 2016; Harel et al., 2016; Lague, 2014),
and numerical modelling studies showing that *m*∕*n* = 0.5 leads to
unrealistic relief structures (Kwang and Parker, 2017).

To test the relative efficacy of our methods for calculating concavity
indices, we first run each method on a series of numerically simulated
landscapes in which the *m*∕*n* ratio is prescribed. We employ a simple
numerical model, following Mudd (2016), where channel
incision occurs based on Eq. (13). For computational efficiency,
we do not include any other processes (e.g. hillslope diffusion) within our
model. The elevation of the model surface therefore evolves over time
according to

$$\begin{array}{}\text{(16)}& {\displaystyle \frac{\partial z}{\partial t}}=U-K{A}^{m}{S}^{n},\end{array}$$

where *U* is the uplift rate. Fluvial incision is solved using the algorithm
of Braun and Willett (2013), where the drainage area is computed using the D8
flow direction algorithm to improve speed of computation and the topographic
gradient is calculated in the direction of steepest descent. In our model, we
perform a direct numerical solution of Eq. (16)
where *n*=1 and use Newton–Raphson iteration where *n*≠1. These
simulations are performed using the MuddPILE numerical model
(Mudd et al., 2017), first used by Mudd (2016). We set
the north and south boundaries of the model domain to fixed elevations,
whereas the east and west boundaries are periodic. Our model domain is 30 km
in the *X* direction and 15 km in the *Y* direction, with a grid resolution
of 30 m. This allows us to test the methods of estimating *m*∕*n* on several
drainage basins in each model domain, and at a resolution comparable to that
of globally available DEMs.

In order to test the methods' ability to identify the correct *m*∕*n* value, we
ran a series of numerical experiments with varying *m*∕*n* ratios: $m/n=\mathrm{0.5}$, $m/n=\mathrm{0.35}$, and $m/n=\mathrm{0.65}$. For each ratio, we also performed
simulations with varying values of *n*, as the *n* exponent has been shown to
impact the celerity of transient knickpoint propagation through the channel
network (Royden and Perron, 2013). Crucially,
Royden and Perron (2013) showed that when *n* is not unity, upstream
propagating knickpoints will erase information about past base level changes
encoded in the channel profiles. This may cloud selection of the correct
*m*∕*n* ratio, but Lague (2014) and Harel et al. (2016) have
suggested many, if not most, natural landscapes have evidence for an *n*
exponent that is not unity. Therefore, we ran simulations with *n*=1, *n*=2,
*n*=1.5, and *n*=0.66 for each *m*∕*n* ratio, varying *m* accordingly (see
the Supplement for details of each model run).

We initialised the model runs using a low relief surface that is created
using the diamond–square algorithm (Fournier et al., 1982). We found
this approach resulted in drainage networks that contained more topological
complexity than those initiated from simple sloping or parabolic surfaces.
Our aim was to test the ability of each method to extract the correct *m*∕*n*
ratio without assuming that the landscapes were in steady state; therefore,
each simulation was forced with varying uplift through time to ensure that
the channel networks were transient.

Each model was run with a baseline uplift rate of 0.5 mm yr^{−1}, which
was increased by a factor of 4 for a period of 15 000 years, then
decreased back to the baseline for another 15 000 years. For the runs with
*n*=2, the cycles were set to 10 000 years, which was necessary to preserve
evidence of transience, as knickpoints propagate more rapidly through the
channel network as *n* increases. Relief is very sensitive to model
parameters, and we found in numerical experiments that basin geometry was
sensitive to relief, mirroring the results of Perron et al. (2008).
We wanted modelled landscapes to have comparable relief and similar basin
geometry across our simulations to ensure similar landscape configurations
for different values of *m*, *n*, and *m*∕*n*. We therefore calculated the
*χ* coordinate and solved Eq. (5) to find the *K*
value for each modelled landscape that produced a relief of 200 m at the
location with the greatest *χ* value given an uplift rate of
0.5 mm yr^{−1}.

We analysed these model runs using each of the methods of estimating the
best-fit *m*∕*n* outlined in Sect. 2.2. We extracted a channel network
from each model domain using a contributing area threshold of 9×10^{5} m^{2}. We performed a sensitivity analysis of the methods to this
contributing area threshold (see the Supplement) and found that
the estimated best-fit *m*∕*n* ratios were insensitive to the value of the
threshold.

Drainage basins were selected by setting a minimum and maximum basin area, 9×10^{6} and 4.5×10^{7} m^{2}, respectively; these values were
chosen so extracted basins represented a good balance between the number of
extracted basins and the number of tributaries in each basin. Nested basins
were removed, as were basins that bordered the edge of the model domain. We
exclude basins on the domain boundaries as the calculation of the *χ*
coordinate for the integral profile analysis is dependent on drainage area,
which may not be realistic at the edge of the domain. Elimination of basins
on the edge of the DEM is essential for real landscapes, as a basin beheaded
by raster clipping will have incorrect *χ* values, and we wanted to ensure
both simulations and analyses on real basins used the same extraction
algorithms. For each basin, we identified the best-fit concavity index
predicted in five ways (as described in the methods section): (i) regression
of all *χ*–elevation data; (ii) use of *χ*–elevation data
processed by our method of sampling points with the bootstrap method;
(iii) minimisation of the disorder metric from *χ*–elevation data using a
similar technique to Hergarten et al. (2016); (iv) regression of all
slope–area data; and (v) regressions through slope–area data for individual
segments of the main stem. For all but the final method, the analyses use all
tributaries in the basins.

Figure 5 shows the spatial distribution of the
predicted *m*∕*n* ratio for a series of basins from these cyclic model runs,
where *m*∕*n* = 0.35, 0.5, and 0.65, and *n*=1. We also plot the *m*∕*n*
ratio predicted for each basin from all methods with varying values of *n*,
an example of which is shown in Fig. 6.
Our modelling results show that for each value of *m*∕*n* ratio tested, the
method using all *χ* data identifies the correct ratio for every basin in
the model domain. The bootstrap approach provides an estimate of the error on
the best-fit *m*∕*n* ratio for each basin:
Fig. 6 shows that there is no error on the
predicted *m*∕*n* ratio, meaning that an identical *m*∕*n* ratio is predicted
with each iteration of the bootstrap approach. The slope–area methods, in
contrast, show more variation in the predicted *m*∕*n* ratio for each value of
*m*∕*n* and *n* tested (Figs. 5 and
6). Furthermore, the segmented slope–area
data show a higher uncertainty in the predicted *m*∕*n* ratio compared to the
other methods. The results of the model runs for all values of *m*∕*n* and *n*
are presented in the Supplement.

Alongside these temporally transient scenarios, we also wished to test the
ability of each method to identify the correct *m*∕*n* ratio in spatially
heterogeneous landscapes, simulating the majority of real sites where
lithology, climate, or uplift are generally non-uniform. Therefore, we
performed additional runs where *m*∕*n* = 0.5, *n*=1, but *U* and *K*
varied in space. We generated the model domains using the same diamond–square
initial condition as the spatially homogeneous runs. For the run with
spatially varying *K*, we calculate the steady-state value of *K* required to
produce a surface with a relief of 400 m and an uplift rate of
1 mm yr^{−1} using the same method as for the previous runs. From this
baseline value of *K*, we calculated a maximum *K* value which is 5 times
that of the baseline. We then created 10 “patches” within the initial
model domain where *K* was assigned randomly between the baseline and the
maximum. These are rectangular in shape with *K* values that taper to the
baseline *K* over 10 pixels. We acknowledge this pattern is not very
realistic but emphasise that the aim is not to recreate real landscapes but
rather to confuse the algorithms for quantifying the concavity index. This
allows us to test if they can still detect modelled concavity indices even if
we violate some of the assumptions implicit in our theoretical framework.

For the spatially varying uplift run, we varied uplift in the N–S direction by modelling it as a half sine wave:

$$\begin{array}{}\text{(17)}& U={U}_{\mathrm{A}}\mathrm{sin}\left(\right(\mathit{\pi}y)/L)+{U}_{min},\end{array}$$

where *y* is the northing coordinate and *L* is the total length of the model
domain in the *y* direction, *U*_{A} is an uplift amplitude, set to
0.2 mm yr^{−1}, and *U*_{min} is a minimum uplift, expressed at the
north and south boundaries, of 0.2 mm yr^{−1}. Both scenarios, with
spatially varying erodibility and uplift, were run to approximately steady
state; the maximum elevation change between 15 000-year printing intervals
was less than a millimetre.

Inherent in each collinearity-based method of quantifying the most likely
*m*∕*n* ratio is the assumption that *U* and *K* do not vary in space
(Perron and Royden, 2013); our spatially heterogeneous experiments
therefore violate basic assumptions of the integral method. These conditions,
however, are likely true in virtually all natural landscapes. Therefore, our
aim here was to test if we could recover *m*∕*n* ratios from numerical
landscapes that are more similar to real landscapes than those with spatially
homogeneous *U* and *K*.

Figure 7 shows the distribution of predicted
*m*∕*n* ratios for the runs with spatially varying *K* and *U* from both the
integral bootstrap approach and the slope–area method. In comparison to our
model runs where *K* and *U* were uniform, each method performs worse at
identifying the correct *m*∕*n* ratio of 0.5. However, in both model runs, the
integral methods identified the correct ratio in a higher proportion of the
drainage basins than the slope–area methods. Furthermore, the distribution
of *m*∕*n* predicted by the integral methods reaches a peak at the correct *m*∕*n*
ratios of 0.5, suggesting that even in spatially heterogeneous landscapes the
methods can still be applied. Our run with the random distribution of
erodibility patches shows that the correct calculation of the *m*∕*n* ratio is
highly dependent on the spatial continuity of *K*; in basins contained within
a single patch (e.g. basins 4, 5, and 6), the integral profile method
correctly identified the *m*∕*n* ratios. Figure 8 shows
example *χ*–elevation plots at varying *m*∕*n* ratios for basin 2, which
encompasses several patches with varying *K* values. Within this basin,
tributaries that drain a patch with the same *K* value are still collinear in
*χ*–elevation space. Based on these results, we suggest that, in real
landscapes, monolithologic catchments should be analysed wherever possible in
order to select an appropriate concavity index.

4 Constraining concavity indices in real landscapes

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Our numerical modelling results suggest that the integral profile analysis is
most successful in identifying the correct concavity index out of the entire
range of *m*∕*n* and *n* values tested. However, these modelling scenarios
cannot capture the range of complex tectonic, lithologic, and climatic
influences present in nature. Therefore, we repeat our analyses on a range of
different landscapes with varying climates, relief structures, and
lithologies, to provide some examples of the variation in the concavity index
predicted using each method. For each field site, topographic data were
obtained from OpenTopography, using the seamless DEM generated from NASA's
Shuttle Radar Topography Mission (SRTM) at a grid resolution of 30 m. The
Supplement contains metadata for each site so readers can extract
the same topographic data used here.

In order to demonstrate the ability of the methods to constrain concavity
indices in a relatively homogeneous landscape, we first analyse the Loess
Plateau in northern China. The channels of the Loess Plateau are incising
into wind-blown sediments that drape an extensive area of over
400 000 km^{2} (Zhang, 1980) and can exceed 300 m thickness
(Fu et al., 2017). The plateau is underlain by the Ordos Block,
a succession of non-marine Mesozoic sediments which has undergone stable
uplift since the Miocene (B. Wang et al., 2017; Yueqiao et al., 2003).
Although there have been both recent (Wang et al., 2016) and historic
(Wang et al., 2006) changes in sediment discharge from the plateau,
the friable substrate means that channel networks and channel profiles might
be expected to adjust quickly to perturbations in erosion rate. Indeed,
Willett et al. (2014) suggested, based on differences in the *χ*
coordinate across drainage divides, that the channel networks in large
portions of the plateau are geomorphically stable. The stable tectonic
setting and homogeneous, weak substrate of the Loess Plateau make an ideal
natural laboratory for testing our methods on relatively homogeneous channel
profiles.

We ran each of the methods on an area of the Loess Plateau approximately
11 000 km^{2} in size near Yan'an, in the Chinese Shaanxi province
(Fig. 9a). We find relatively good agreement between
both the *χ* and slope–area methods of estimating the most likely concavity
index. Figure 9b shows the probability distribution of
concavities determined from the population of the most likely concavities
from each basin (i.e. it does not include underlying uncertainty in each
basin), but the peaks of these curves lie at a *θ*≈0.45 using
both the disorder method and the bootstrap method, 0.4 using all slope–area
data, and at approximately 0.5 using the all *χ* data method. This level
of agreement gives the worker some confidence that channel steepness analyses
in this area of the Loess Plateau using reference concavities between 0.4 and
0.5 should give an accurate representation of the relative steepness of the
channels.

As well as determining the best-fit concavity index for the landscape as a
whole, we can also examine the channel networks in individual basins:
Fig. 9c shows the *χ*–elevation profiles for an
example basin. In this basin, the tributaries are well aligned with the trunk
channel at the most likely *θ* of 0.45, both using all the *χ* data and
with the bootstrap approach. In our explorations of different landscapes, the
Loess Plateau is the landscape that most resembles the idealised landscapes
that we find in our model simulations. The Loess Plateau is notable for the
homogeneity of its substrate over a large area; most locations on Earth are
not as homogeneous.

Many studies analysing the steepness of channel profiles are focused in areas
where external factors, such as lithology or tectonics, are not uniform.
Here, we select an example of a landscape with two dominant lithologic types in a
location along the Oregon coast near the town of Waldport, Oregon
(Fig. 10). The Oregon Coast Range is dominated by
the Tyee Formation, made up primarily of turbidites deposited during the
Eocene (Heller et al., 1987). In addition to these sedimentary
units, our selected landscape also contains the Yachats Basalt, which erupted
mostly as sub-areal flows between 3 and 9 m in thickness during the late
Eocene (Davis et al., 1995). Erosion rates inferred from ^{10}Be
concentrations in stream sediments are between 0.11 to 0.14 mm yr^{−1}
(Bierman et al., 2001; Heimsath et al., 2001), similar to rock uplift
rates of 0.05–0.35 mm yr^{−1} inferred from marine terraces
(Kelsey et al., 1994). Short-term erosion rates derived from stream
sediments fall into the range of 0.07 to 0.18 mm yr^{−1}
(Wheatcroft and Sommerfield, 2005), leading a number of authors to suggest that
the Coast Range is in topographic steady state, where uplift is balanced by
erosion (Reneau and Dietrich, 1991). Thus, our site contains a clear
lithologic contrast but has been selected to minimise spatial variations in
uplift or erosion rates.

We find that whereas basins developed on basalt have a relatively uniform
concavity index of approximately 0.7, the most likely concavity indices in
the sandstone show considerably more scatter
(Fig. 10b), with a lower average *θ*. We
present these data as an example of spatially varying concavity indices as
a function of lithology. This is consistent with results of
VanLaningham et al. (2006), who found high concavities in volcanic
rocks around Waldport but lower elsewhere, and found high values of the
concavity index in sedimentary rock but with a higher degree of scatter along
the Oregon Coast Range.

Whipple and Tucker (1999) suggested that the concavity index is
controlled primarily by discharge–drainage area and channel width–drainage
area relationships, which may be influenced by lithology, but other authors
have found systematic variations in concavity indices with lithology
(Duvall et al., 2004; Lima and Flores, 2017; VanLaningham et al., 2006). Lima and Flores (2017) suggested that the thickness of
basalt flows could influence concavity indices, with different knickpoint
propagation mechanisms in massive versus thinly bedded flows.
Duvall et al. (2004) suggested that having hard rocks in headwaters
and weak below might influence concavity indices, which may be tested by
comparing concavity indices in both monolithologic basins and basins with
mixed lithology. The *χ* profiles in basin 17
(Fig. 10c) are notable because this basin features
two bedrock types: basalt in the lower reaches and sandstone in the
headwaters. If the selected concavity index is too high, tributaries will
fall below the trunk channel in *χ*–elevation space. In
Fig. 10c, *θ* is chosen to reflect the typical
value of the basalt basins, and tributary channels in the sandstone fall
below the trunk channel, meaning that changes in the concavity index can be
seen within basins. We therefore suggest that workers must be cautious when
using a reference concavity index in determining channel steepness indices in
basins with heterogeneous lithology.

The steepness of channel profiles and presence of steepened reaches (knickpoints) in tectonically active areas can reveal spatial patterns in the distribution of erosion and/or uplift (Densmore et al., 2007; DiBiase et al., 2010; Vanacker et al., 2015) and have the potential to allow identification of active faults (Kirby and Whipple, 2012). However, these systematic spatial patterns in channel steepness may challenge our ability to constrain the concavity index. Our third example is in a tectonically active landscape where we have found spatial variations in the most likely concavity index between catchments proximal to active normal faults. We explore a series of basins draining across faults in the Sperchios Basin, Gulf of Evia, Greece (Fig. 11), predominantly cut into clastic sediments (Eliet and Gawthorpe, 1995). Previous work (Whittaker and Walker, 2015) has demonstrated that catchment morphology reflects interaction with these faults. The rivers are typically characterised by convex longitudinal profiles that commonly have two knickpoints. The upper set of knickpoints is attributed to the initiation of faulting and the resulting growth of topography. The lower set of knickpoints is interpreted as the result of subsequent increase (3–5 ×) in throw rate due to fault linkage (Whittaker and Walker, 2015). The elevations of each group of knickpoints both scale with footwall relief, suggesting that fault throw rates scale with fault segment length. The Gulf of Evia therefore represents a natural experiment where uplift and erosion rates are expected to vary both temporally and spatially.

Steep, smaller catchments tend to drain across the footwalls of these faults,
whilst larger catchments drain the landscape behind the faults, through the
relay zones between fault segments. We derived the best-fit concavity index
for each catchment following each of the five methods
(Fig. 12). Given the presence of knickpoints along the
river profiles, it is not appropriate to derive the concavity index by linear
regression of all log[*S*]–log[*A*] data. We find that the concavity index
estimated from segmented slope–area analysis is highly variable between
catchments (Fig. 12, inset), with a tendency toward
abnormally large values, exceeding the upper range of values typically
predicted by incision models (Whipple and Tucker, 1999). Values of
*θ* derived using the *χ* methods are predicted to be relatively low,
typically 0.1–0.6 (Fig. 12), and whilst the *χ*
methods do not agree perfectly, they do covary, and are for the most part
within uncertainty of each other (with the exception of basins 1 and 20).

A number of authors have suggested that in both highly transient and rapidly
eroding landscapes processes other than fluvial plucking or abrasion become
important in shaping the channel profile, such as debris flows and plunge
pool erosion (DiBiase et al., 2015; Haviv et al., 2010; Scheingross and Lamb, 2017; Stock and Dietrich, 2003). Recent work has suggested
that retreat of vertical waterfalls may result in similar concavities to
fluvial incision processes operating in lower gradient settings
(Shelef et al., 2018), whereas debris flows have been shown to lead
to channels with low concavity indices (Stock and Dietrich, 2003). The lowest
values of *θ* = 0.1 at the Evia site typically occur for the small,
steep catchments draining across the footwalls of the fault segments (e.g.
basin 10; Fig. 13), with higher *θ* values typical
for catchments that do not cross faults or those that cross relay zones
(e.g. basin 7; Fig. 13). Plots of *χ*–elevation such
as in Fig. 13 demonstrate that there can be considerable
variability in the morphology of tributaries as they respond to adjustment in
the trunk channel.

Our aim here is not to provide a comprehensive examination of the topography
and tectonic evolution of the Sperchios Basin
(Whittaker and Walker, 2015) but to demonstrate the impact of tectonic
transience on our ability to quantify the concavity index. Low concavity
indices in steep, small catchments draining across the faults may reflect the
contribution of debris flow processes to valley erosion at smaller drainage
areas, which tends to lead to lower apparent concavity indices in the
topography (Stock and Dietrich, 2003). Additionally, these catchments may in
effect behave as fluvial hanging valleys (Wobus et al., 2006b).
Concavity indices derived using the bootstrap points method are in all cases
equal to or lower than values derived using all *χ* data. This is
noteworthy because of the difference in how tributaries are weighted between
the two techniques. Using all *χ* data, longer tributaries have more
influence on the calculation of the most likely concavity index, whereas the
bootstrap points method weights each tributary equally (since the same
number of points are sampled on each tributary). Thus, if the steepness of
the channels at low drainage area is influenced by debris flow processes
(Stock and Dietrich, 2003), we would expect this to be more influential on the
derived concavity index when using the bootstrap points method, resulting in
lower *θ* values.

Finally, it is recognised that transient landscapes are likely settings for
drainage network reorganisation (Willett et al., 2014). In the absence
of lithologic variability, climate gradients and tectonic transience,
gradients in *χ* in the channel network between adjacent drainage basins
are predicted to indicate locations where drainage divides are migrating
(toward the catchment with higher *χ*) and drainage network reorganisation
is ongoing (Willett et al., 2014). On the other hand, numerical
simulations suggest that spatial variability in uplift is more important
than temporal gradients in uplift rates (Whipple et al., 2016).
Rivers draining across normal fault systems are often routed through the
relay zones between fault tips, where uplift rates are lowest, capturing and
rerouting much of the drainage area above the footwall
(Paton, 1992). In the Sperchios Basin, this has
resulted in strong gradients in *χ* across topographic divides
(Fig. 14), particularly between the large catchments
draining the landscape behind the footwall (which have likely been gaining
drainage area), and the short, steep catchments draining across the footwall
(which have likely been truncated).

Our analysis of the topography in the Sperchios Basin, whilst not exhaustive,
highlights that river profiles and the resulting concavities (and/or
*k*_{sn}) derived from topography are not alone sufficient to
interpret the history of landscape evolution but must be considered
alongside other observational data and in the context of a process-based
understanding of landscape evolution and tectonics.

5 Conclusions

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For over a century, geomorphologists have sought to link the steepness of bedrock channels to erosion rates, but any attempt to do so requires some form of normalisation. This normalisation is required because in addition to topographic gradient, the relative efficacy of incision processes is thought to correlate with other landscape properties that are a function of drainage area, such as discharge or sediment flux. Theory developed over the last four decades suggest that the concavity index may be used to normalise channel gradient, and over the last two decades many authors have compared the steepness of channels normalised to a reference concavity index derived from slope–area data (Kirby and Whipple, 2001; Snyder et al., 2000). In recent years, an integral method of channel analysis has also been developed (Perron and Royden, 2013) that can complement slope–area analysis and via alignment of tributaries provide an independent test of the concavity index.

In this contribution, we have developed a suite of methods to quantify the
most likely concavity index using both slope–area analysis and the integral
method. In addition to traditional slope–area methods, we also present
methods of analysing *χ*-transformed channel networks that do not require
the profiles to be linear from source to outlet but constrain concavity-based
collinearity of each tributary and the trunk channel. In a second
method, we quantify uncertainty on the predicted value of *θ* using a
subset of points on the tributary network that are randomly assigned within a
bootstrap sampling framework. We also test a similar disorder metric that is
a minimum when tributaries and trunk channel are most collinear. We test
these methods against idealised, modelled landscapes that obey the stream
power incision model but have been subject to transient uplift, as well as
spatially varying uplift and erodibility, where the concavity index is
imposed through the ratio of the exponents *m* and *n*.

We find that *χ*-based methods are best able to reproduce the concavity
indices imposed on the model runs. We recommend users calculate the most
likely concavity indices using the bootstrap and disorder methods as these
provide estimates of uncertainty, although the disorder method is the most
tightly constrained of the *χ*-based methods. The most likely concavities
determined from *χ*-based methods on transient landscapes have low
uncertainty because the transient models do not violate any assumptions
underlying *χ*-based methods. The spatially variable model runs, where
assumptions of the *χ* method are violated, still perform better than
slope–area analysis in extracting the correct concavity index. This gives us
some confidence that in real landscapes, where non-uniform uplift and
spatially varying erodibility are likely pervasive, calculated concavities
may still reveal useful information about the incision processes. Thus, we
hope future workers can calculate reliable, reproducible concavity indices
for many small basins in regions with spatially varying uplift, climate, or
lithology to test if patterns in the concavity index can be linked to
variations in these landscape properties.

Code and data availability

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Code and data availability.

Code used for analysis is located at https://doi.org/10.5281/zenodo.1291889 (Mudd et al., 2018) and https://doi.org/10.5281/zenodo.1291889 (Mudd et al., 2017). Scripts for visualising the results can be found at https://github.com/LSDtopotools/LSDMappingTools (last access: 18 June 2018). We have also provided documentation detailing how to install and run the software, which can be found at https://lsdtopotools.github.io/LSDTT_documentation (last access: 18 June 2018). As part of the Supplement, we have also provided 45 example parameter files which can be used to reproduce the results of all analyses performed in this study. All topographic data used in this contribution are available through http://Opentopography.org (last access: 22 November 2017; NSF OpenTopography Facility, 2013).

Supplement

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Supplement.

The supplement related to this article is available online at: https://doi.org/10.5194/esurf-6-505-2018-supplement.

Author contributions

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Author contributions.

SMM and FJC wrote the code for the analysis, performed the numerical modelling and wrote the visualisation software. SMM, FJC, BG, and MDH performed the analysis on the real landscapes. SMM wrote the paper with contributions from other authors.

Competing interests

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Competing interests.

The authors declare that they have no conflict of interest.

Acknowledgements

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Acknowledgements.

We thank Rahul Devrani, Jiun Yee Yen, Ben Melosh, Julien Babault,
Calum Bradbury, and Daniel Peifer for beta testing the software. Reviews by
Liran Goren and Roman DiBiase substantially improved the
paper. This work was supported by Natural Environment Research Council grants
NE/J009970/1 to Simon M. Mudd and NE/P012922/1 to Fiona J. Clubb.
Boris Gailleton was funded by European Union Initial Training grant 674899 –
SUBITOP. All topographic data used for this study were SRTM 30 m data
obtained from http://Opentopography.org (last access: 22 November 2017;
NSF OpenTopography Facility, 2013).

Edited by: Jens
Turowski

Reviewed by: Roman DiBiase and Liran Goren

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