Project 19:
Aeolian sediment grain movement is influenced by a range of different physical (size, density, and shape) and bed properties (roughness, moisture, and hardness) (Rotnicka et al., 2022). To quantify, and ultimately predict the movement of grains within a vertical (height) profile, experiments involving laboratory wind tunnels with fixed parameters and field experiments have been used. These experiments have shown that the best model fit to describe the vertical mass distribution of sediment is characterized by an exponential decay function. However, when such models are applied to natural systems, the model fit becomes much more complicated. We are currently collecting sediment at different heights using passive sediment samplers at an active dune field (located on the Navajo Nation) that comprises two different grain populations. Our goal for this work is to quantify the mass-to-height relationship and determine what is the best function (exponential, quadratic exponential, or a sum of exponentials) to use when establishing a model for grain movement and distribution for an area that is bimodal in both composition and grain size.
Aeolian sediment collected from passive sediment samplers will need to be separated into grain size categories by using a combination of different size sieves. Material from each size fraction will then be weighed, followed by estimating modal percentage of the two grain populations (i.e., felsic and basaltic sand) present in each size fraction, all recorded in a spreadsheet. We will then calculate the mass flux (amount of sediment passing through a unit area over a period of time) and create vertical (height) profiles. As we continue to measure samples and plot our results, we will work towards determining what function best fits the vertical mass distribution of a dune field that is bimodal. There are two components to this workload- lab work and data analysis. Lab work comprises sieving, weighing, and estimating modal percentages for samples and will be carried out using NAU’s Rock Lab. This lab has automatic sieve shakers and a number of different sieve mesh sizes that the student will use. Data analysis will comprise plotting the data and evaluating different best-fit models in Excel.
This project will result in the student gaining experience in both sample and data analysis, as well as contributing an important dataset to the aeolian science community. Working towards establishing a best-fit model for a complex natural system will assist in the improvement of mass flux and sediment transport computer models and better understand particle-modified wind flows. Depending on the applicant’s interest, preparation and presentation of this work could result in a first-authored abstract and poster presentation at an international science conference (e.g., Lunar and Planetary Science Conference, or equivalent). Additionally, if data is used in a future peer-reviewed publication, they would contribute as a co-author.