REFINING ESTIMATES AND UNCERTAINTIES IN PALEOSLOPE ANALYSIS

Reconstructing slopes of ancient sediment transport systems forms an important basis for understanding landscape dynamics, crustal motions, and potentially climate. In order to capture the full potential of data for paleoslopes, a complete treatment of their uncertainty is required. Here we present a recently developed model for paleoslope inversion based on a modern dataset for fluvial bankfull flow conditions. We then explore its limitations based on uncertainty and compare this model to other existing methods.

We derive a Bayesian model to appropriately capture the variability in the modern dataset for channel slope, S, as a function of bankfull channel depth, Hbf, and median bed sediment grain size, D50. It is given by log S = -2.1 + 0.25 log D50 -1.1 log Hbf. The dataset includes 541 measurements of each parameter and results in a robust characterization of uncertainty. In detail, the dispersion of the modern data about the best-fit trend results in an error estimate that is approximately an order of magnitude. This error is significantly larger than potential errors associated with measurements of median grain size and bankfull channel depth from strata. For this reason we conclude that this method of inversion for paleoslope is limited by the explanatory power of the modern relationship on which it is based.

Other methods for paleoslope inversion are based on simplifications for bankfull Shields stress, on details of transport conditions for sediment in suspension, or on geomorphic regional curves. Each of these methods with various advantages and disadvantages is directly comparable. Ultimately, none of them has a superior control on uncertainty of paleoslope estimates. Currently, all methods are limited by relatively low explanatory power of the modern relationships on which they are respectively based. In all cases, errors from measurements of grain size and channel depth are likely small enough to be ignored during inversion for paleoslope.