Disentangling composite forcings on river channel steepness in heterogeneous landscapes
Early geomorphological studies quickly realised the close relationship between the geometrical characteristics of river networks with the geological, tectonic and climatic context of the regions the river crosses - the most striking example perhaps being the upstream migration of steepened reaches (or “knickpoints”), which rejuvenate landscapes following a base level change via faulting or eustatic variations. Amongst all the morphologies related to the river network, channel gradient has been a particularly important one: following the observation that declivity enhances erosion, any forcing affecting the channel slope potentially also affects erosion and creates a signal that can be interpolated through space and time to reconstruct climatic or tectonic events modulated by local lithology. Parallel to these qualitative observations, geomorphological studies developed tools and metrics to quantify the large-scale shape of river long profiles. Rivers systematically steepen towards the headwaters, which makes the direct use of river slope difficult, so several authors successfully developed a semi-empirical relationship describing the systematic increase of channel gradient with a power-law relationship. This relationship links the steepening component (ks) to the rate at which slope increases as drainage area decreases (θ); later followed by a normalised river length coordinate Χ. Later work on this topic demonstrated a positive relationship between these metrics and the underlying tectonic or lithologic forcings, allowing the systematic comparison of different field sites across the world and large-scale testing of the early qualitative observations. The rise in availability of global scale Digital Elevation Models and external proxies to quantify exhumation/erosion rates (e.g. thermochrononometers, CRN), has made such large scale studies increasingly fast and accessible, making the geomorphometrics described above widely used as spatial and temporal interpolations of tectonics and climatic variations. However, (i) the metrics are affected by different forcings, meaning that a single morphology can be generated by a range of different factors and hence potentially misinterpreted, and (ii) θ varies spatially and will determine the normalisation parameter to calculate ks and Χ. This thesis focuses on developing and applying tools to investigate the different expression of these metrics in heterogeneous landscapes. First, I describe an algorithm to extract and quantify knickpoint morphologies. The aim of this algorithm is to allow the objective comparison of knickpoints in different contexts. They are quantified using ks and sudden changes in elevation in order to detect the location and the magnitude of the knickpoints. I explore the performance and limitations of the algorithm in different settings, and describe how to constrain and interpret this novel work. Secondly, I explore the spatial variability of θ in order to achieve multiple goals. (i) I develop and thoroughly test a method to finely explore the variations of θ while also assessing how a specific θ value fits a watershed in case a non-optimal value has to be fixed. (ii) From the observed range of θ variation, I analytically and numerically explore how a non-optimal θ can affect ks and Χ, respectively. I identify cases where this caveat can lead to the generation/exaggeration of spurious signals or to the diminution/inversion of existing ones. Thirdly, I apply such algorithms, alongside with field measurements and observations, in a heterogeneous landscape: the eastern Carpathians. The eastern Carpathians are highly heterogeneous with (i) sharp lithologic contrasts, (ii) high spatial and temporal contrasts in vertical motion, and (iii) spatially variable θ. I demonstrate with systematic comparison of steepness across θ with lithologic and tectonic forcings that in that case lithology is the dominant forcing expressed in ks, although tectonic forcing can be retrieved. I discuss and successfully integrate my conclusions into local tectonic models and show larger scale implications for interpreting these metrics.