2015 GSA Annual Meeting in Baltimore, Maryland, USA (1-4 November 2015)

Paper No. 201-9
Presentation Time: 10:15 AM


HEIMSATH, Arjun M., School of Earth and Space Exploration, Arizona State University, ISTB4, Tempe, AZ 85287, arjun.heimsath@asu.edu

Quantifying soil production and transport rates and processes is a key aspect of most landscape evolution studies that focus upon hillslopes. Methodologies range from relatively straightforward, low cost studies like sediment traps and basin volume estimates to sophisticated and expensive studies using chemical and physical tracers and isotope geochemistry. Inherent in all methodologies are assumptions and simplifications that enable any measurement to be translated to a rate and interpreted in terms of process mechanics. For example, quantifying soil production using in situ cosmogenic nuclides measured in the parent material beneath an upland soil mantle depends on assuming a locally steady state soil thickness (i.e. neither thinning or thickening with time). Similarly, utilizing hillslope-scale sediment traps or landscape-scale basin analyses depends on assumptions of steady state processes and constraining the timescales represented by the captured sediment. As our analyses of landscapes have expanded into the details of how spatial and temporal gradients of all driving variables (lithology, climate, tectonics, distribution of biota, etc.) influence hillslope evolution, our adherence to the assumptions and simplifications necessitated by our methods becomes questionable. Here I synthesize recent work quantifying soil production and transport processes using a range of different methodologies to assess how well the governing assumptions have held up. Specifically, comparison of studies using longer time scale geochemical “tracers” and the like with shorter time scale, physical trap based studies highlight the strengths and weaknesses of such disparate approaches. Our own work using cosmogenic nuclides across a wide range of climatic and tectonic gradients illustrates well the challenges of adhering to governing assumptions.