Ecogeomorphic state variables and phase-space construction for quantifying the evolution of vegetated aeolian landscapes

Baas, Andreas C.W. and Nield, Joanna M. (2010) Ecogeomorphic state variables and phase-space construction for quantifying the evolution of vegetated aeolian landscapes. Earth Surface Processes and Landforms, 35 (6), 717-731. (doi:10.1002/esp.1990).

Abstract

Cellular automaton modelling for the simulation of dune field formation and evolution has developed progressively in aeolian geomorphology in the last decade or so. A model that incorporates the effects of vegetation and its interactions with geomorphic landscape development - the Discrete Ecogeomorphic Aeolian Landscapes (DECAL) model - can replicate a number of important visual and qualitative aspects of the complex evolution of aeolian dune landscapes under the influence of vegetation dynamics in coastal environments. A key challenge in this research area is the analysis and comparison of both simulated and real-world vegetated dune landscapes using objective and quantifiable principles. This study presents a methodological framework or protocol for numerically quantifying various ecogeomorphic attributes, using a suite of mathematically defined landscape metrics, to provide a rigorous and statistical evaluation of vegetated dune field evolution. Within this framework the model parameter space can be systematically explored and simulation outcomes can be methodically compared against real-world landscapes. Based on a simplified scenario of parabolic dunes developing out of blow-outs the resulting dune field realizations are investigated as a function of variable growth vigour of two simulated vegetation types (pioneer grass and successional woody shrub) by establishing a typological phase-diagram of different landscape classes. The set of simulation outcomes furthermore defines a higher-dimensional phase-space, whose axes or dimensions can be interpreted by analysing how individual ecogeomorphic landscape metrics, or state variables, contribute to the data distribution. Principal component analysis can reduce this to a visual three-dimensional (3D) phase-space where landscape evolution can be plotted as time-trajectories and where we can investigate the effects of changing environmental conditions partway through a simulation scenario. The use of landscape state variables and the construction of a 3D phase-space presented here may provide a general template for quantifying many other eco-geomorphic systems on the Earth's surface.

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Contributors

Author: Joanna M. Nield

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