[1] This letter presents a self-organising cellular automaton model capable of simulating the evolution of vegetated dunes with multiple types of plant response in the environment. It can successfully replicate hairpin, or long-walled, parabolic dunes with trailing ridges as well as nebkha dunes with distinctive deposition tails. Quantification of simulated landscapes with eco-geomorphic state variables and subsequent cluster analysis and PCA yields a phase diagram of different types of coastal dunes developing from blow-outs as a function of vegetation vitality. This diagram indicates the potential sensitivity of dormant dune fields to reactivation under declining vegetation vitality, e.g. due to climatic changes. Nebkha simulations with different grid resolutions demonstrate that the interaction between the (abiotic) geomorphic processes and the biological vegetation component (life) introduces a characteristic length scale on the resultant landforms that breaks the typical self-similar scaling of (un-vegetated) bare-sand dunes. Citation: Baas, A. C. W., and J. M. Nield (2007), Modelling vegetated dune landscapes, Geophys. Res. Lett., 34, L06405,

No abstract

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 selforganization 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. Figure 3. Relationship between values of U * and c/q (solid blue line) and the range of appropriate slab heights (1/7·5 to 1/13) for use in the model. This figure is available in colour online at www.interscience.wiley.com/journal/espl 730 Figure 4. Flow diagram of the DECAL model. Yellow shaded sequence indicates the algorithm of an individual slab displacement. Green section represents the annual vegetation growth response. This figure is available in colour online at Figure 8. Nebkha dune formation under varying sediment flux (top) and speed (bottom) conditions compared with the standard conditions used for Figure 6; transport direction left to right. This figure is available in colour online at

Ephemeral aeolian sand strips are commonplace on beaches. Their formation during high energy sand transport events often precedes the development of protodunes and their dynamics present interesting feedback mechanisms with surface moisture patterns. However, due to their temporary nature, little is known of their formation, mobility or the specifics of their interaction with beach surface characteristics. Similarly surface moisture has an important influence on sediment availability and transport in aeolian beach systems, yet it is difficult to quantify accurately due to its inherent variability over both short spatial and temporal scales. Whilst soil moisture probes and remote sensing imagery techniques can quantify large changes well, their resolution over mainly dry sand, close to the aeolian transport threshold is not ideal, particularly where moisture gradients close to the surface are large. In this study we employed a terrestrial laser scanner to monitor beach surface moisture variability during a three and a half hour period after a rain event and investigated relationships between bedform development, surface roughness and surface moisture. Our results demonstrate that as the beach surface dries, sand transport increases, with sediment erosion occurring at the wet/dry surface boundary, and deposition further downwind. This dynamic structure, dependent upon changing surface moisture characteristics, results in the formation of a rippled sand strip and ultimately a protodune. Our findings highlight dynamic mobility relationships and confirm the need to consider transient bedforms and surface moisture across a variety of scales when measuring aeolian transport in beach settings. The terrestrial laser scanner provides a suitable apparatus with which to accomplish this.

[1] Surface roughness plays a key role in determining aerodynamic roughness length (z o ) and shear velocity, both of which are fundamental for determining wind erosion threshold and potential. While z o can be quantified from wind measurements, large proportions of wind erosion prone surfaces remain too remote for this to be a viable approach. Alternative approaches therefore seek to relate z o to morphological roughness metrics. However, dust-emitting landscapes typically consist of complex small-scale surface roughness patterns and few metrics exist for these surfaces which can be used to predict z o for modeling wind erosion potential. In this study terrestrial laser scanning was used to characterize the roughness of typical dust-emitting surfaces (playa and sandar) where element protrusion heights ranged from 1 to 199 mm, over which vertical wind velocity profiles were collected to enable estimation of z o . Our data suggest that, although a reasonable relationship (R 2 > 0.79) is apparent between 3-D roughness density and z o , the spacing of morphological elements is far less powerful in explaining variations in z o than metrics based on surface roughness height (R 2 > 0.92). This finding is in juxtaposition to wind erosion models that assume the spacing of larger-scale isolated roughness elements is most important in determining z o . Rather, our data show that any metric based on element protrusion height has a higher likelihood of successfully predicting z o . This finding has important implications for the development of wind erosion and dust emission models that seek to predict the efficiency of aeolian processes in remote terrestrial and planetary environments.