UNIFYING COSMOGENIC EXPOSURE DATING AND COSMOGENIC GEOMORPHOLOGY WITH PROCESS MODELING

Cosmogenic nuclides accumulate at predictable rates in near-surface rocks. Measuring the concentrations of cosmogenic nuclides in these rocks allows estimation of the ages of landforms and the rates of geomorphic processes acting on those landforms.

Most past studies have estimated only one of these parameters, assuming that the other is known. For example, measurements of cosmogenic nuclide concentrations in an exposed rock surface could be used to estimate the timing of deglaciation of the surface, assuming that the erosion rate of the surface is known to lie within certain limits. Conversely, the same concentration measurements could be used to estimate the post-glacial erosion rate of the surface, if the time of deglaciation is known approximately.

Here, we demonstrate that geomorphic process modeling can provide simultaneous estimates of ages and rates of geomorphic processes for moraines. Moraines are dated using the concentrations of cosmogenic nuclides in boulders on the moraine surface. These boulders are derived from bedrock or preexisting till sheets, and may contain significant concentrations of cosmogenic nuclides when deposited on moraines. The exposure dates yielded by these boulders overestimate the age of the moraine. Some boulders are covered with till when first deposited, and are subsequently exhumed by slope processes. Exhumed boulders give exposure dates that underestimate the age of the moraine. We have constructed numerical models of both these processes. Our model of boulder exhumation follows the work of others in this field, although our model of boulder incorporation is novel.

By fitting our geomorphic process models to the statistical distributions of cosmogenic exposure dates from individual moraines, we can estimate the moraines' ages, the rates at which sediment moves downhill on these moraines, and the depth to which the glaciers that built the moraines eroded their beds.

We present sample fits of our models to published data sets, and discuss the implications of the model parameter estimates yielded by these fits.