Rock strength and structural controls on fluvial erodibility: Implications for drainage divide mobility in a collisional mountain belt

Abstract

Highlights 9• We estimate rock strength, erodibility and drainage divide mobility in the High Atlas 10Mountains 11• The weakest rock-type in the High Atlas is up to two orders of magnitude more erodible than 12 the strongest 13• In gently deformed horizontal strata of the sedimentary cover the drainage divide is mobile 14• Faulted and folded metamorphic sedimentary bedrock coincide with a stable drainage divide 15• Exhumation of crystalline basement forces the drainage divide into the centre of exposed 16 basement 17 Abstract 18 Numerical model simulations and experiments have suggested that when migration of the main 19 drainage divide occurs in a mountain belt, it can lead to the rearrangement of river catchments, 20 rejuvenation of topography, and changes in erosion rates and sediment flux. We assess the 21 progressive mobility of the drainage divide in three lithologically and structurally distinct groups of 22 bedrock in the High Atlas (NW Africa). The geological age of bedrock and its associated tectonic 23 architecture in the mountain belt increases from east to west in the study area, allowing us to 24 track both variations in rock strength and structural configuration which influence drainage 25 mobility during erosion through an exhuming mountain belt. Collection of field derived 26 measurements of rock strength using a Schmidt hammer and computer based extraction of river 27 channel steepness permit estimations of contrasts in fluvial erodibilities of rock types. The 28 resulting difference in fluvial erodibility between the weakest and the strongest lithological unit is 29 up to two orders of magnitude. Published evidence of geomorphic mobility of the drainage divide 30 indicates that such a range in erodibilities in horizontal stratigraphy of the sedimentary cover may 31 lead to changes in erosion rates as rivers erode through strata, leading to drainage divide 32 migration. In contrast, we show that the faulted and folded metamorphic sedimentary rocks in the 33 centre of the mountain belt coincide with a stable drainage divide. Finally, where the strong 34 igneous rocks of the crystalline basement are exposed after erosion of the covering meta-35 sediments, there is a decrease in fluvial erodibility of up to a factor of three, where the drainage 36 divide is mobile towards the centre of the exposed crystalline basement. The mobility of the 37 drainage divide in response to erosion through rock-types and their structural configuration in a 38 mountain belt has implications for the perception of autogenic dynamism of drainage networks 39 and fluvial erosion in mountain belts, and the interpretation of the geomorphology and 40 downstream stratigraphy. 41 42

Formation of Longitudinal River Valleys and the Fixing of Drainage Divides in Response to Exhumation of Crystalline Basement

“…The area (m) and slope (n) exponent are 0.6 and 1.5 respectively and give a concavity index ( ) of 0.4 which falls within the range of concavities (i.e., 0.35-0.6) found by studies that have analyzed the relationship between drainage area and local slope (Kirby & Whipple, 2012;Whipple & Tucker, 1999). Finally, we set a spatially uniform erodibility coefficient of 2 × 10 −6 m 0.1 .yr −1 in the range of values deduced for the sedimentary cover of mountain ranges (Gallen, 2018;Zondervan et al, 2020). When run to steady state, this setup generates an asymmetric range with the main drainage divide located in the northern part of the model, comparable to many doubly vergent mountain belts where rock uplift rates are higher on the retro-wedge due to advection of rock from the regions of maximum accretion (e.g., Taiwan and Olympic Mountains; Willett et al, 1993).…”

Growing topography due to contrasting rock types in a tectonically dead landscape

“…Gilbert, 1877;Howard and Kerby, 1983;Stock and Montgomery, 1999;Whipple and Tucker, 1999;Jansen et al, 2010;Bursztyn et al, 2015) in concert with a number of other controls, including climate conditions and runoff efficiency, channel width scaling, extreme hydrologic events, and frequency of debris flow (e.g. Snyder et al, 2000;Whipple et al, 2000a;Kirby and Whipple, 2001;Duvall et al, 2004;Zondervan et al, 2020b). All of these factors are encapsulated in a dimensional coefficient of erosion efficiency (K) in the commonly used stream power model (Howard and Kerby, 1983):…”

“…where E is the long-term fluvial erosion; A is the upstream contributing drainage area; S is the local channel gradient; and m and n are positive exponents that depend on incision process, channel hydraulics, and rainfall variability (Whipple and Tucker, 1999). Whereas most variables in the stream power model can be derived from DEM data, the fluvial erosion efficiency coefficient (K) cannot be measured directly, and thus the computing of K demands constraints on timing and/or rates of river evolution (Zondervan et al, 2020b). We have a limited understanding of how K varies in different geomorphic conditions and what controls its variability due to the few studies that derived absolute constraints on K and confounding between the multiple controls encoded in K (Snyder et al, 2000;Whipple et al, 2000a;Duvall et al, 2004;Harel et al, 2016;Zondervan et al, 2020b).…”

“…More sophisticated versions of the stream power model may explicitly account for different controls in K (e.g. DiBiase and Whipple, 2011;Campforts et al, 2020;Zondervan et al, 2020b), yet such an approach requires specific data of an adequate resolution, such as river hydraulic geometry, rock strength measurements, and hydrological data to resolve spatial and temporal runoff variability, which are not readily available.…”

“…However, the fluvial erosion efficiency coefficient (K) as calculated here incorporates other effects besides rock strength and precipitation rates, such as channel hydraulic geometry and sediment load (e.g. Snyder et al, 2000;Duvall et al, 2004;Zondervan et al, 2020b). Whereas in the Appalachians effects on fluvial erosional efficiency such as channel width and sediment load have been shown to vary as a function of rock type (e.g.…”