  A dry decade in the Navajo Nation has killed vegetation, dessicated soils,
and released once-stable sand into the wind. This sand now covers one-third of
the Nation's land, threatening roads, gardens and hundreds of homes. Many arid
regions have similar problems: global warming has increased dune movement
across farmland in Namibia and Angola, and the southwestern US. Current dune
models, unfortunately, do not scale well enough to provide useful forecasts for
the $\sim$5\% of land surfaces covered by mobile sand. We test the ability of
two deep learning algorithms, a GAN and a CNN, to model the motion of sand
dunes. The models are trained on simulated data from community-standard
cellular automaton model of sand dunes. Preliminary results show the GAN
producing reasonable forward predictions of dune migration at ten million times
the speed of the existing model.
