• vegetation;
  • parabolic dunes;
  • nebkhas;
  • cellular automata;
  • self-organization


Vegetation plays an important role in shaping the morphology of aeolian dune landscapes in coastal and semi-arid environments, where ecogeomorphic interactions are complex and not well quantified. We present a Discrete ECogeomorphic Aeolian Landscape model (DECAL) capable of simulating realistic looking vegetated dune forms, permitting exploration of relationships between ecological and morphological processes at different temporal and spatial scales. The cellular automaton algorithm applies three simple rules that lead to self-organization of complex dune environments, including nebkhas with distinctive deposition tails that form in association with mesquite-type shrubs, and hairpin (long-walled) parabolic dunes with trailing ridges that evolve from blowouts in association with vegetation succession. Changing the conditions of simulations produces differing landscapes that conform qualitatively to observations of real-world dunes. The model mimics the response of the morphology to changes in sediment supply, vegetation distribution, density and growth characteristics, as well as initial disturbances. The introduction of vegetation into the model links spatial and temporal scales, previously dimensionless in bare-sand cellular automata. Grid resolutions coarser than the representative size of the modelled vegetation elements yield similar morphologies, but when cell size is reduced to much smaller dimensions, the resultant landscape evolution is dramatically different. The model furthermore demonstrates that the relative response characteristics of the multiple vegetation types and their mutual feedback with geomorphological processes impart a significant influence on landscape equilibria, suggesting that vegetation induces a characteristic length scale in aeolian environments. This simple vegetated dune model illustrates the power and versatility of a cellular automaton approach for exploring the effects of interactions between ecology and geomorphology in complex earth surface systems. Copyright © 2007 John Wiley & Sons, Ltd.