Abstract. Self-organized landscape patterning can arise in response to multiple processes. Discriminating among alternative patterning mechanisms, particularly where experimental manipulations are untenable, requires process-based models. Previous modeling studies have attributed patterning in the Everglades (Florida, USA) to sediment redistribution and anisotropic soil hydraulic properties. In this work, we tested an alternate theory, the self-organizing canal (SOC) hypothesis, by developing a cellular automata model that simulates pattern evolution via local positive feedbacks (i.e., facilitation) coupled with a global negative feedback based on hydrology. The model is forced by global hydroperiod that drives stochastic transitions between two patch types: ridge (higher elevation) and slough (lower elevation). We evaluated model performance using multiple criteria based on six statistical and geostatistical properties observed in reference portions of the Everglades landscape: patch density, patch anisotropy, semivariogram ranges, power-law scaling of ridge areas, perimeter area fractal dimension, and characteristic pattern wavelength. Model results showed strong statistical agreement with reference landscapes, but only when anisotropically acting local facilitation was coupled with hydrologic global feedback, for which several plausible mechanisms exist. Critically, the model correctly generated fractal landscapes that had no characteristic pattern wavelength, supporting the invocation of global rather than scale-specific negative feedbacks.
Evaluation of ehe Geoecological Factors Influence on the Foundations’ Precipitation, Located on Anisotropic Soil Bases
