Influence of lithology on hillslope morphology and response to tectonic forcing in the northern Sierra Nevada of California

Abstract

[1] Many geomorphic studies assume that bedrock geology is not a first-order control on landscape form in order to isolate drivers of geomorphic change (e.g., climate or tectonics). Yet underlying geology may influence the efficacy of soil production and sediment transport on hillslopes. We performed quantitative analysis of LiDAR digital terrain models to examine the topographic form of hillslopes in two distinct lithologies in the Feather River catchment in northern California, a granodiorite pluton and metamorphosed volcanics. The two sites, separated by <2 km and spanning similar elevations, were assumed to have similar climatic histories and are experiencing a transience in landscape evolution characterized by a propagating incision wave in response to accelerated surface uplift c. 5 Ma. Responding to increased incision rates, hillslopes in granodiorite tend to have morphology similar to model predictions for steady state hillslopes, suggesting that they adjust rapidly to keep pace with the incision wave. By contrast, hillslopes in metavolcanics exhibit high gradients but lower hilltop curvature indicative of ongoing transient adjustment to incision. We used existing erosion rate data and the curvature of hilltops proximal to the main channels (where hillslopes have most likely adjusted to accelerated erosion rates) to demonstrate that the sediment transport coefficient is higher in granodiorite (8.8 m 2 ka À1 ) than in metavolcanics (4.8 m 2 ka À1 ). Hillslopes in both lithologies get shorter (i.e., drainage density increases) with increasing erosion rates.

How does grid-resolution modulate the topographic expression of geomorphic processes?

“…The predicted values of the sediment transport coefficient (D) for the 1 m data fall within the range of values compiled by Hurst et al (2013c) and estimated for the Oregon Coast Range and Gabilan Mesa by Roering et al (1999) and Roering et al (2007). This suggests that this method can produce useful estimates of D when employing high-resolution topography.…”

“…Range and Santa Cruz Island datasets exhibit an increase in estimated D, all of the values for each location fall within the range of values for D compiled by Hurst et al (2013c).…”

“…Its importance is highlighted by an increasing number of studies using this relationship as a proxy for erosion rate (Pelletier et al, 2011;Hurst et al, 2013c, b;Grieve et al, 2016b). Hilltop curvature can also be used to constrain the sediment transport coefficient of a landscape where an independent constraint on erosion rate is available (Hurst et al, 2013c).…”

Laser vision: lidar as a transformative tool to advance critical zone science

“…Meter-and sub-meter-scale time-varying processes, often derived from TLS, have been quantified in the response of point bar and bank morphodynamics (Lotsari et al, 2014) and in the formation of micro-topography due to feedbacks with biota (e.g., Roering et al, 2010;Harman et al, 2014). Examples of larger scale change detection applications, typically ALS-derived, include measuring changes in stream channel pathways resulting from Holocene climate change and anthropogenic activities (e.g., Day et al, 2013;Kessler et al, 2012;James et al, 2012;Belmont et al, 2011), rates of change in migrating sand dunes (Pelletier, 2013), the influence of lithology and climate on hillslope form (e.g., Marshall and Roering, 2014;Hurst et al, 2013;Perron et al, 2008;West et al, 2014), and channel head formation (e.g., Pelletier et al, 2013;Pelletier and Perron, 2012;Perron and Hamon, 2012). Automated tools to identify geomorphic features (e.g., floodplains, terraces, landslides) and transitional zones (e.g., hillslope-to-valley, floodplain-tochannel) have been used in conjunction with high-resolution elevation data sets from lidar, including Geonet 2.0 (Passalacqua et al, 2010), ALMTools (Booth et al, 2009), and TerrEX (Stout and Belmont, 2014).…”

“…However, we caution that one scientist's signal may be another's noise (Tarolli, 2014). Signal recognition may involve smoothing at one scale to quantify a relevant landscape metric, such as hillslope curvature (and derived erosion rates; Hurst et al, 2013), which in turn limits valuable information at another scale, such as hydrologically driven surface roughness or the spacing of tree-driven bedrock disruption (Roering et al, 2010;Hurst et al, 2012). Overall, lidar data sets retain the promise of up-or downscaling feedbacks among multiple processes that are just beginning to be fully utilized.…”