self-organization – Travis Swanson

Parsing autogenic and allogenic processes in aeolian stratigraphy

I am beyond excited to present highly collaborative work that begins to parse records of autogenic processes and allogenic forcings preserved within set-scale aeolian architecture in two companion articles: (1) numerical experiments (preprint) and (2) the scour-fill dominated Jurassic Page Sandstone, Arizona (preprint).

synStrat — Set-scale aeolian architecture from a numerical model of dynamic dune topography driven by steady allogenic boundary conditions. Color represents time of deposition.

In the first companion article, a reduced complexity model of aeolian dune strata-formation is developed and applied to explore the roles of autogenic processes on the preservation of allogenic sourced from three sets of external environmental forcing. In each scenario, rapid dune growth is found to completely cannibalize early dune deposits, thus shredding any records of early allogenic or autogenic signals. However, later dune deposits are found to contain commingled autogenic and allogenic signals. This theoretical work frames five working hypotheses surrounding the nature of aeolian dune deposits for future workers to explore and discuss! The source code (Matlab) will reproduce all figures in this article is available here. A Python version is planned.

cardenasJSR2018 — Page Sandstone. Early dune deposits (smaller sets in B,D) are preferentially preserved in antecedent lows in paleotopography.

In the companion article, collaborator Benjamin Cardenas leads interpretation of set-scale aeolian architecture within multiple exposures of the Page Sandstone (Jurassic) near Page Arizona, USA. This work interprets the set-scale architecture of the Page Sandstone to record multiple transgressions of the Carmel Sea. Drying, and associated decreases in base-level between transgressions liberates significant quantities of sand. After drying, dune growth and scour are interpreted to cannibalize early dune deposits, except for those created in antecedent lows in paleotopography. Ongoing dune motion in the dry sand sea creates scour and fill architecture which is thought to characterize autogenic dune behavior. Eventually, the Carmel Sea completely transgresses the sand accumulation, and shuts down the aeolian system at the end of the Page (pun intended).

Exploring the completeness of the aeolian record within synthetic stratigraphy

To be submitted soon!

Abstract: A reduced complexity model aeolian dune stratification model is developed and applied to explore the role of dune morphodynamics in the creation of synthetic sections of aeolian stratigraphy and shredding of environmental signals originating from three sets of environmental forcing: 1) steady transport capacity, 2) steady bed aggradation and variable transport capacity, and 3) steady transport capacity and bed aggradation. In each scenario, the forward motion of initial, highly disorganized dunes generates a significant record exclusively containing autogenic signals that arise from early dune growth, deformation, and merger. However, continued dune growth scours deeply, and shreds all records of early dunes. Afterward, dunes self-organize into groups of dunes. Forward motion of dune groups create, truncate, and amalgamate sets and co-sets of cross-strata, quickly forming a second, significantly more robust stratigraphic record, which preserves a comingling of signals sourced from ongoing autogenic processes and each scenario’s specific set of environmental forcings. Although the importance of self-organization on modeled aeolian stratification is clear in the few presented scenarios, self-organization maybe throttled via variability within environmental forcings. Therefore, additional work is warranted as this numerical experiment only begins to sample possible sets of environmental forcing, boundary conditions, and initial conditions, geomorphic responses, and consequential preservation.

Here’s a sneak peak of the simulations:

The videos below so the co-evolution of dune topography and stratigraphy for three different model scenarios. In each video, bedform stratigraphy is vertically exaggerated 100x. Additionally, bedform topography is reduced 20x. η* and x* are non-dimensional vertical and horizontal scales, respectively. η* represents the fraction of equilibrium dune height, and similarly, x* represents the number of equilibrium dune wavelengths. Enjoy!

1) Steady transport capacity

2) Steady bed aggradation and time-varying transport capacity

3) Steady bed aggradation and transport capacity

The dynamics of spur-bearing bedforms

Bedform spurs are formed by helical vortices that trail from the lee surface of oblique segments of bedform crest lines. Trailing helical vortices quickly route sediment away from the lee surface of their parent bedform, scouring troughs and placing this bed material into the body of the spur. Here’s a video of a single bedform spur:

When present, spur-bearing bedforms and their associated trailing helical wakes exert tremendous control on bedform morphology by routing enhanced sediment transport between adjacent bedforms. Field measurements collected at the North Loup River, Nebraska, and flume experiments described in previous studies demonstrate that this trailing helical vortex-mediated sediment transport is a mechanism for bedform deformation, interactions and transitions between two-dimensional and three-dimensional bedforms. Below is a time lapse image of many spur bearing bedforms. Watch as they pause and surge due to spur-routing of sediment transport.

Click on the picture of the manuscript heading to visit the publisher’s webpage and access more information about spur bearing bedform dynamics, including more videos!

A new surface model for aeolian dune topography

Aeolian dune topography arises from a highly non-linear interaction between sediment transport, topography, and boundary shear stress. To explore the growth of aeolian dunes under a variety of boundary conditions, a new surface model for aeolian bedform topography is adapted from a surface model of subaqueous bedform topography (Jerolmack and Mohrig, 2005)[1]. The resulting modeling framework approximates the dynamic motions of aeolian bedform topography driven by bedform field boundary conditions; namely, different distributions of sediment transport direction and investigating bedform growth with and without the constraint of a fixed sediment source area (modeled as a fixed elevation boundary). The rates at which modeled aeolian bedforms grow and morphologically mature are found to be highly sensitive to the chosen boundary conditions. Click on the image of the manuscript header to visit the journal’s website and read more about this study.

The videos below show four permutations of two boundary conditions: uni- and bi-modal distributions of sediment transport direction are used to grow bedform topography with and without the constraint of a sediment source area. In these videos hot colors indicate higher topography and cooler colors indicate lower topography.

Uni-modal distribution of sediment transport direction with periodic boundaries

Uni-modal distribution of sediment transport moving sediment from a fixed source

Bi-modal distribution of sediment transport direction with periodic boundaries

Bi-modally distributed sediment transport moving sediment from a fixed source

The aeolian bedform surface model code is malleable and readily modified for exploratory study of bedform topography that inherits morphological traits from aeolian bedform field boundary conditions. A version of the source code for these simulations is available from MATGEO. However, a newer version of this software will be made available via a public repository on GitHub, shortly.

[1] Jerolmack DJ, Mohrig D (2005) A unified model for subaqueous bed form dynamics. Water Resour Res 41(12):W12421. doi:10.1029/2005WR004329 (PDF link)

Self organization of aeolian dunes

Aeolian dune motion is thought to be driven by an annual cycle of sediment-transporting wind events. Each wind event drives uneven motion of dune crestlines, yet dune crestlines align as a trend to an annual cycle of wind . Understanding the variability in dune motion over such a cycle aids the interpretation of aeolian cross-stratification, often available only in the limiting exposure of core and outcrop.

Digital elevation models obtained by light detection and ranging (lidar, Fig. 1) are used to estimate dune brink motion and sediment flux along the sinuous crestlines of crescentic dunes at White Sands gypsum dune field (south-central New Mexico, USA) over an annual cycle of wind.

: Fig. 1 Time lapse animation of dune elevation of study area within White Sands, NM. Duration is approximately 3 yrs.

By using an edge detection algorithm, dune brink motion (Fig. 1) can be used to estimate local values of sediment flux. These estimations reveal that dune motion and sediment flux are very well described by a circular normal distribution when sampled using a spatial window of approximately the size of six average dunes. At this scale, the distribution of erratic dune motion is symmetrically distributed around the average lee surface dip direction. Therefore, uneven motion of dune crest lines offset, and the geometric self-organization of dune crests as a trend line is maintained.

: Fig. 2 Dune brink movement occurring over slightly more than a year is shown by the colormapped circles. The elevation of the aeolian dunes is shown by the grayscale.