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).
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.
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).
Many thanks are owed to the CoRE group (Especially Zane Jobe) at the Department of Geology and Geological Engineering at the Colorado School of Mines for inviting me to give a departmental seminar in their Van Tuyl lecture series. For this seminar, we will explore the response of the expansive system of barrier islands and peninsulas along the Texas coast to a wide survey of sea-level rise rates and barrier overwash configurations.
In celebration of Dr. John Anderson‘s upcoming retirement at Rice University, I have the privilege of giving a short research talk during an all-day research symposium, appropriately entitled JohnFest!. JohnFest! is composed of a series of talks given by John’s past and present students and post-docs. For this special occasion, I am excited to be presenting my postdoctoral work, which John has supported and mentored over the past year and a half, a reduced complexity model of Texas’ coastal barrier system.
Just in time for the Industry-Rice Earth Science Symposium 2018, the newest iteration of the reduced complexity coastal barrier model was summarized as a poster presentation. Be sure to click on the image to the right to download a high resolution jpeg image of the poster!
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
For AGU Fall Meeting 2017 in New Orleans, I gave a talk on the application of a simple morphodynamic model to forward model the response of coastal barriers (islands and peninsulas) to spatially variable sea-level rise over centuries. Within the model, coastal barrier geomorphology is simplified to a suite of characteristic scales and surface processes are simplified to parameterized expressions that characterize geomorphic responses to relative sea level rise. The abstract for this presentation is in an earlier post (Getting ready for AGU 2017), and a PDF of the presentation is available here (opens in a new window)!
Occasionally, data are published in the limiting form of a few values and corresponding cumulative percentiles. For example, grain size data are frequently published in terms of the 10th, 50th, and 90th cumulative percentiles. However, sometimes it’s nice to have an approximation of what a model distribution would look like if constrained to fit these points. Based on the solution given by Matt Tearle, here’s a short function below that gives an approximation of the mean and standard deviation of a log-normal distribution given at least two value-percentile pairs:
function m = prc2dist(p,q,guess)
%fit a log normal distribution to given p (percentiles), q (value) pairs,
%and a guess vector where guess=[guessMean guessStd].
%Output: m is a vector with elements, m(1) = mean,
% and m(2) = standard deviation,
% of the fitted distribution.
% see: help logninv for more information.
% p = [0.1, 0.8]; %percentiles (cumulative probability)
% q = [1, 100]; %values
% guess = [1, 1]; %initial guess of mean and stdev, respectively.
% m = prc2dist(p,q,guess) % returns mean and standard dev of log-normal distribution
p = p(:);
q = q(:);
guess = guess(:)';
if length(p) ~= length(q)
fprintf('value and percentile vectors must be the same length.\n');
m = nan(1,2);
elseif length(p) < 2
fprintf('value and percentile vectors must be > 2 elements in length.\n');
m = nan(1,2);
g = @(p,b,q) logninv(p,b(1),b(2))-q;%distance between guess and input
f = @(b) norm(g(p,b,q));%calculate l2 norm of distance vector
m = fminsearch(f,guess);