Complexity of Earth Surface System Evolutionary Pathways

Phillips, Jonathan D.

doi:10.1007/s11004-016-9642-1

Cited by 14 publications

(13 citation statements)

References 99 publications

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“…While most of the focus in the landscape evolution models remains on tectonic uplift and precipitation patterns, lithology conspicuously emerged as the primary control through our fractal analysis. Further, the topographic characterization through the fractal patterns also presents an opportunity to analyse the inherent nonlinearity in the geomorphic system, as the fractal patterns can be mathematically linked to several forms of nonlinear complexity (Phillips, 2003(Phillips, , 2016.…”

Section: Discussionmentioning

confidence: 99%

Process inference from topographic fractal characteristics in the tectonically active Northwest Himalaya, India

Sahoo

Singh

Jain

2020

Earth Surf Processes Landf

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Topography evolves under the coupled effect of exogenic and endogenic governing factors, and their scale-(in)variant dynamics. This results in a self-affine topography across a finite range, with a characteristic fractal dimension. Fractal analysis has been used to classify geological terrains having distinct litho-tectonic settings. However, process-based understanding of the fractal behaviour of a natural landscape is still limited. The current study aims to substantiate and expand upon the present knowledge of topographic response to the complex actions of the governing factors using fractal characteristics. We examined the association between the litho-tectonic, climatic settings and the fractal characteristics of the topography in the tectonically active Northwest Himalaya. Our analysis was carried out in three separate sectors having diverse litho-tectonic settings. We used the roughnesslength method to calculate the fractal parameters (fractal dimension, D; ordinate intercept, q). The Higher and the Lesser Himalaya were found to be characterized by low D and high q, while the tectonically active Sub Himalaya was found to have moderate D and low q. The southernmost foreland alluvial plains were characterized by high D and low q. Clusters of the fractal parameters were found to be consistent in spatial pattern across the three sectors. Our results showed that the geological-geomorphological settings and the associated processes (e.g. uplift, erosion and diffusion) can be well inferred using the fractal characteristics of the topography. Further, our results implied first-order control of lithology in sustaining and shaping the topographic geometry (both its amplitude and texture) in the tectonically active Northwest Himalaya. The spatial distribution of the fractal parameters also suggested the secondary control of tectonic uplift and, to a much lesser extent, mean annual rainfall on the topographic geometry. These results collectively point to the role of complex actions of the governing factors in the landscape evolution process.

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Section: Discussionmentioning

confidence: 99%

Process inference from topographic fractal characteristics in the tectonically active Northwest Himalaya, India

Sahoo

Singh

Jain

2020

Earth Surf Processes Landf

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“…With that, the method is applicable to the results from other (soil-)landscape evolution models or erosion models as well (Temme et al, 2017). Calculating evolutionary pathways is not even limited to soil or geomorphological systems (Phillips, 2016). As long as quantitative spatial and temporal data of model output is present, evolutionary pathways can be determined.…”

Section: Applicability For (Soil-)landscape Evolution Modellingmentioning

confidence: 99%

Evolutionary pathways in soil-landscape evolution models

Meij

2021

Preprint

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Abstract. Soils and landscapes can show complex, non-linear evolution, especially under changing climate or land use. Soil-landscape evolution models (SLEMs) are increasingly equipped to simulate the development of soils and landscapes over long timescales under these changing drivers, but provide large data output that can be difficult to interpret and communicate. New tools are required to analyse and communicate large model output. In this work, I show how spatial and temporal trends in previously published model results can be summarized and conceptualized with evolutionary pathways, which are possible trajectories of the development of soil patterns. Simulated differences in rainfall and land use control progressive or regressive soil development and convergence or divergence of the soil pattern. These changes are illustrated with real-world examples of soil development and soil complexity. The use of evolutionary pathways for analysing the results of SLEMs is not limited to the examples in this paper, but they can be used on a wide variety of soil properties, soil pattern statistics and models. With that, evolutionary pathways provide a promising tool to analyse and communicate soil model output, not only for studying past changes in soils, but also for evaluating future spatial and temporal effects of soil management practices in the context of sustainability.

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“…Likewise, debate remains over how to unequivocally discern which data distributions best fit power laws as compared to other, related regression models such as lognormal distributions (e.g., Newman, 2005; Pumain, 2006; McKelvey, 2011, among many others). Different experts have shown evidence that both soil bodies and soilscapes are complex systems (e.g., Phillips, 2013, 2016, 2017) and that they show fractal properties (e.g., Caniego, Ibáñez, & San José, 2006; Ibáñez, Pérez‐Gómez, & San José Martínez, 2009a; San‐José Martínez & Caniego, 2013) (Figure 3a and b). In fact, Phillips (1998) demonstrated that Hans Jenny's factors of soil formation cannot be solved using classical linear mathematical tools, whereas Jenny's state factor equation can be understood in the frame of non‐linear dynamical systems theory.…”

Section: The Nature and Uncertainty Of Power Laws (Natural Artificiamentioning

confidence: 99%

“…In fact, Phillips (1998) demonstrated that Hans Jenny's factors of soil formation cannot be solved using classical linear mathematical tools, whereas Jenny's state factor equation can be understood in the frame of non‐linear dynamical systems theory. We assume that such a link occurs between power laws and complex systems, including the whole earth surface system, such as the structure of drainage basis, landforms and regolith, among many others (e.g., Phillips, 1998, 2016), in view of the fact that most of these resources are also non‐linear and there are numerous interactions between them. Non‐linear and/or complex systems are the product of the interactions between the diverse elements that make up a system.…”

Section: The Nature and Uncertainty Of Power Laws (Natural Artificiamentioning

confidence: 99%

Exploring the scaling law of geographical space: Gaussian versus Paretian thinking

Ibáñez

Ramírez‐Rosario

Fernández‐Pozo

et al. 2020

European J Soil Science

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A debate has occurred concerning the laws of scale and the fractal nature of geographical space. Biodiversity and pedodiversity studies show the emergence of fractal structures such as taxa‐area relationships. Biodiversity and pedodiversity are natural resources, although some consider pedotaxa to be artificial. The studies carried out to date emphasize that many pedodiversity and biodiversity spatial patterns converge at the same regularities. Many of these studies used natural resources maps and their digital databases. Information is extracted from the taxa types contained in each polygon and the areas covered. However, the structure of the maps (number, area, fragmentation, etc.) has rarely been a matter of study. When map structure was studied, intriguing similarities were observed in pedodiversity and biodiversity analyses. To understand whether these similarities also appear in other types of spatial entities that are more artificial, a review of geospatial analyses that studied topics such as urban maps, land system maps, etc., was undertaken. The main variables in these maps are manmade and/or combinations of natural resources data layers. Regularities detected in the geospatial information of these latter topics also seem to conform to results obtained when analysing natural resources maps such as soils, rock types, landforms, plant communities, etc. Thus, some geographers consider the idea that there are far more small things/objects than larger ones across several orders of magnitude in geographic space to be a law. Some geographers also contend that the classical “Gaussian thinking” and its statistical tools should be replaced by a “Paretian thinking” and its related statistical tools. This paper analyses the above topics as well as the lack of adequate data and types of cognitive maps we use in our modern scientific society, supporting the conjecture that that we should include Paretian thinking in our research at least in the same way we use Gaussian thinking. Highlights Long tail distributions, power laws and fractal‐like structures are ubiquitous in geospatial analysis. Scale invariance of natural and artificial map structures seem to be the rule. The Paretian approach appears to be more appropriate than the Gaussian approach for many purposes. Our cognitive maps and brain processing follow Paretian thinking in many instances.

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Complexity of Earth Surface System Evolutionary Pathways

Cited by 14 publications

References 99 publications

Process inference from topographic fractal characteristics in the tectonically active Northwest Himalaya, India

Process inference from topographic fractal characteristics in the tectonically active Northwest Himalaya, India

Evolutionary pathways in soil-landscape evolution models

Exploring the scaling law of geographical space: Gaussian versus Paretian thinking

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