A lower-than-expected saltation threshold at Martian pressure and below

Anomalous Scaling of Aeolian Sand Transport Reveals Coupling to Bed Rheology

Predicting transport rates of windblown sand is a central problem in aeolian research, with implications for climate, environmental, and planetary sciences. Though studied since the 1930s, the underlying many-body dynamics is still incompletely understood, as underscored by the recent empirical discovery of an unexpected third-root scaling in the particle-fluid density ratio. Here, by means of grain-scale simulations and analytical modeling, we elucidate how a complex coupling between grain-bed collisions and granular creep within the sand bed yields a dilatancy-enhanced bed erodibility. Our minimal saltation model robustly predicts both the observed scaling and a new undersaturated steady transport state that we confirm by simulations for rarefied atmospheres.

A distinct ripple-formation regime on Mars revealed by the morphometrics of barchan dunes

Sand mobilized by wind forms decimeter-scale impact ripples and decameter-scale or larger dunes on Earth and Mars. In addition to those two bedform scales, orbital and in situ images revealed a third distinct class of larger meter-scale ripples on Mars. Since their discovery, two main hypotheses have been proposed to explain the formation of large martian ripples-that they originate from the growth in wavelength and height of decimeter-scale ripples or that they arise from the same hydrodynamic instability as windblown dunes or subaqueous bedforms instead. Here we provide evidence that large martian ripples form from the same hydrodynamic instability as windblown dunes and subaqueous ripples. Using an artificial neural network, we characterize the morphometrics of over a million isolated barchan dunes on Mars and analyze how their size and shape vary across Mars' surface. We find that the size of Mars' smallest dunes decreases with increasing atmospheric density with a power-law exponent predicted by hydrodynamic theory, similarly to meter-size ripples, tightly bounding a forbidden range in bedform sizes. Our results provide key evidence for a unifying model for the formation of subaqueous and windblown bedforms on planetary surfaces, offering a new quantitative tool to decipher Mars' atmospheric evolution.

From Macro- to Microscale: A combined modelling approach for near-surface wind flow on Mars at sub-dune length-scales

The processes that initiate and sustain sediment transport which contribute to the modification of aeolian deposits in Mars’ low-density atmosphere are still not fully understood despite recent atmospheric modelling. However, detailed microscale wind flow modelling, using Computational Fluid Dynamics at a resolution of <2 m, provides insights into the near-surface processes that cannot be modeled using larger-scale atmospheric modeling. Such Computational Fluid Dynamics simulations cannot by themselves account for regional-scale atmospheric circulations or flow modifications induced by regional km-scale topography, although realistic fine-scale mesoscale atmospheric modeling can. Using the output parameters from mesoscale simulations to inform the input conditions for the Computational Fluid Dynamics microscale simulations provides a practical approach to simulate near-surface wind flow and its relationship to very small-scale topographic features on Mars, particularly in areas which lack in situ rover data. This paper sets out a series of integrated techniques to enable a multi-scale modelling approach for surface airflow to derive surface airflow dynamics at a (dune) landform scale using High Resolution Imaging Science Experiment derived topographic data. The work therefore provides a more informed and realistic Computational Fluid Dynamics microscale modelling method, which will provide more detailed insight into the surface wind forcing of aeolian transport patterns on martian surfaces such as dunes.

Squeezing Data from a Rock: Machine Learning for Martian Science

Data analysis methods have scarcely kept pace with the rapid increase in Earth observations, spurring the development of novel algorithms, storage methods, and computational techniques. For scientists interested in Mars, the problem is always the same: there is simultaneously never enough of the right data and an overwhelming amount of data in total. Finding sufficient data needles in a haystack to test a hypothesis requires hours of manual data screening, and more needles and hay are added constantly. To date, the vast majority of Martian research has been focused on either one-off local/regional studies or on hugely time-consuming manual global studies. Machine learning in its numerous forms can be helpful for future such work. Machine learning has the potential to help map and classify a large variety of both features and properties on the surface of Mars and to aid in the planning and execution of future missions. Here, we outline the current extent of machine learning as applied to Mars, summarize why machine learning should be an important tool for planetary geomorphology in particular, and suggest numerous research avenues and funding priorities for future efforts. We conclude that: (1) moving toward methods that require less human input (i.e., self- or semi-supervised) is an important paradigm shift for Martian applications, (2) new robust methods using generative adversarial networks to generate synthetic high-resolution digital terrain models represent an exciting new avenue for Martian geomorphologists, (3) more effort and money must be directed toward developing standardized datasets and benchmark tests, and (4) the community needs a large-scale, generalized, and programmatically accessible geographic information system (GIS).