Time Averages, Climatic Change, and Predictability

Burt, Janis M.

doi:10.1111/j.1538-4632.1986.tb00102.x

Cited by 2 publications

(2 citation statements)

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“…The latter were explicitly systems-oriented examinations of geomorphic problems (Phillips 1986, GA;1991, GA;Rhoads 1991, GA), or papers that contribute to the literature on spatial and temporal averaging in climate and terrain studies (Burt 1986, GA; Band 1989, GA).…”

Section: Nds In Geography and Geographical Analysismentioning

confidence: 99%

“…Physical geography contributions in GA that explicitly incorporate NDS theory did not appear until 1993 (Phillips 1993, GA;1997, GA;Outcalt et al 1997, GA), though some earlier papers indirectly contributed to the development of NDS theory in the geosciences. The latter were explicitly systems-oriented examinations of geomorphic problems (Phillips 1986, GA;1991, GA;Rhoads 1991, GA), or papers that contribute to the literature on spatial and temporal averaging in climate and terrain studies (Burt 1986 , GA; Band 1989, GA).…”

Section: Nds In Geography and Geographical Analysismentioning

confidence: 99%

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Spatial Analysis in Physical Geography and the Challenge of Deterministic Uncertainty

Phillips

1999

Geographical Analysis

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The application of complex and nonlinear dynamical systems (NDS) theoy in physical geography and geosciences has proceeded through several stages, and has recently entered a phase where jield-testable hypotheses and historical or mechanistic explanations are being generated. However, there are some fundamental challenges. It seems clear that chaos and dynamical self organization are present, and may be common in earth surface systems, and that these phenomena have spatial manfestations in the landscape. However, NDS the0 y and methods have been formulated primarily in the temporal domain and are typically ill-suited to real-world spatial data. Spatial analytical methods are not generally capable of distinguishing deterministic complexity and uncertainty from noise. Thus, the detection of the signals of complex deterministic dynamics in real landscapes and spatial data is a major challenge. Entropy-based methods of spatial analysis can be directly linked to nonlinear dynamics, and are at present the best available method to approach this problem. However, there is evidence in the spatial analysis literature suggesting that development of techniques to detect deterministic uncertainty is possible. Pending such a breakthrough, three general approaches are described, based on spatial analysis of chronosequences, the characteriziation of changes in spatial structure over time, and the spatial-domain testing of spec@ hypotheses relevant to deterministic uncertainty. Current trends generally suggest a shijl in mathematical modeling and spatial analysis in physical geography away from traditional determinism toward approaches that incorporate locational, historical, and scale contingency.It is not difficult in the late 1990s to cite numerous examples of plausible, interesting, and apparently reasonable mathematical models of earth surface phenomena that exhibit deterministic chaos, self-organization, and other manifestations of complex nonlinear dynamics. While this is of some intrinsic interest, and is of direct relevance to numerical modeling in physical geography, complex model dynamics leave many physical geographers unmoved because of an apparent lack of connection or concrete relevance to real landscapes and earth surface systems. How are such complex dynamics expressed in topography or vegetation patterns or soil geography? What is their signature in a stratigraphic profile? What can complex nonlinear dynamics tell us about the way that landscapes evolve in real time? More to the point, (how) are complex non-

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Section: Nds In Geography and Geographical Analysismentioning

confidence: 99%

Section: Nds In Geography and Geographical Analysismentioning

confidence: 99%