<p>Tectonic, climatic, and anthropogenic forcing generate sediment flux signals that propagate across the Earth&#8217;s surface. Some of these signals get stored in strata but autogenic processes present in the Earth surface active layer can shred (i.e. degrade) and obscure many signals of environmental change prior to stratigraphic storage. In a landmark paper, Jerolmack and Paola (2010) use a numerical rice pile to show that autogenic events in the system saturate at a timescale <em>T</em><sub>x</sub>, which is noted to scale as <em>L<sup>2</sup>/q<sub>in</sub> </em>and corresponds to a red-to-white noise transition. The conceptual utility of this is that those environmental signals with periods less than <em>T<sub>x</sub></em> will experience shredding (unless signal magnitude overwhelms autogenic processes), while signals with periods greater than <em>T<sub>x</sub></em> would be detectable in the output. However, the relationships between signal shredding, preservation and detection are currently not established using physical experiments. Advancing on this work, we use a physical rice pile and find that power spectra generated from efflux time-series exhibit a tripartite geometry defined by red, white and blue noise. The transition between each regime defines two key autogenic timescales: <em>T<sub>rw</sub> </em>and <em>T<sub>wb</sub>. T<sub>rw </sub></em>is defined by the red-to-white noise transition, setting upper bounds on signal degradation, and represents <em>T<sub>x</sub></em> on the power spectra of Jerolmack and Paola (2010), but does not scale with <em>q<sub>in</sub></em>. Whereas signals greater than <em>T<sub>wb</sub></em>, which scales with <em>q<sub>in</sub></em>,<sub> </sub>are unobscured by autogenic noise and show enhanced detectability in the power spectra. We emphasize that while signals greater than <em>T<sub>rw</sub></em> do not experience degradation, they can still be obscured by autogenic noise, unless signal period is greater than <em>T<sub>wb</sub></em><sub>. </sub>This framework can be used to predict the severity of shredding as signals propagate through the Earth surface active layer, and establish robust confidence limits of signal detectability in landscapes and strata.</p>
<p>Landscapes have the ability to transmit environmental signals or inhibit them. The mechanisms by which landscapes do this are largely unquantified, but is probably due to the ability of landscapes to transiently store and release sediment which acts as a medium for energy to propagate. Previous experiments using 1D avalanching rice piles suggest that stochastic collapses can overprint, or shred, periodic sedimentary signals (Jerolmack and Paola (2010), as measured using mass efflux from the experimental rice pile. Jerolmack and Paola (2010) defined a threshold for successful surface signal propagation: Tx, where signals with a period less than Tx are shredded, unless the magnitude of the signal is sufficiently large. We aim to utilise the rice pile to further investigate signal propagation across a landscape, and the thresholds for this, by quantifying inter-particle interactions and the mechanics of how signals propagate using a quasi-2D rice pile model, built using MFiX-DEM code. This open source, physics model utilises individual particles which compose the solid phase whilst treating the fluid as a continuum. The rice grains in the model are represented by spherical particles, where each individual particle, or cluster of particles, can be tracked through each time step using a coordinate axis system, allowing internal dynamics, such as avalanche sizes and sediment residence times, to be quantified. To certify the model replicates the self-organised nature of an experimental rice pile, sensitivity tests were performed by systematically changing two key parameters controlling grain interactions: the friction coefficient and the coefficient of restitution, alongside the sediment flux. To calibrate the results of the sensitivity analysis, mass efflux through time and the corresponding power spectra are compared to real experimental rice pile results and statistical rice pile models. It is hoped this work will provide fundamental insights into how a signal propagates through a landscapes, and how they are shredded in the process. &#160;&#160;</p>
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