Modeling soil system:complexity under your feet

This is an introductory articl

Remote sensing, modelling-based hazard and risk assessment, and management of agro-forested ecosystems

Agricultural and forested landscapes cover large areas over many countries; they are a very important natural resource that needs to be managed sustainably for both the environment and the local communities. Food security, population growth, urbanization, and intensive agricultural development are some of the factors that generate increasing demands for water and land resources in the context of global change. Therefore, potential impacts deriving from a changing climate, from more frequent and intense extreme events, and from anthropogenic activities can pose serious threats to economic infrastructure and development in the coming decades and also severely undermine food, fodder, water, and energy security for a growing global population. Significant recent changes in climate and in the hydrological cycle will impact land suitability for agricultural production and forest ecosystems. In particular, we can expect an increase, in some regions, in the frequency and intensity of extreme weather and weather-related events such as heat waves, floods, wind and snowstorms, droughts, etc. (IPCC, 2012; IPCC, 2021). Furthermore, anthropogenic activities can exacerbate consequences of an unbalanced environment, such as water quality degradation, groundwater depletion, land subsidence, erosion, and sedimentation (Delkash et al., 2018; Tarolli and Straffellini, 2020). Therefore, sustainable management and exploitation of first-order agricultural resources and forested areas, e.g. available land with favourable climate, soil, and water, will become even more important in the lives and activities of people. The 10 original papers included in this special issue address several of these aspects. In particular one review paper provides a general introduction to risk assessment for natural hazards, six papers focus on water- and weather-related hazards (four related to agriculture and two related to water quality at river basin scale), two papers address hazard assessment for the insurance sector, and one paper is related to challenges in agriculture–forest frontiers. The presented researches adopt different types of quantitative and qualitative modelling and spatial analysis and use remote sensing data, when relevant

Comparison of gliding box and box-counting methods in river network analysis

International audienceWe use multifractal analysis to estimate the Rényi dimensions of river basins by two different partition methods. These methods differ in the way that the Euclidian plane support of the measure is covered, partitioning it by using mutually exclusive boxes or by gliding a box over the plane. Images of two different drainage basins, for the Ebro and Tajo rivers, located in Spain, were digitalized with a resolution of 0.5 km, giving image sizes of 617×1059 pixels and 515×1059, respectively. Box sizes were chosen as powers of 2, ranging from 2×4 pixels to 512×1024 pixels located within the image, with the purpose of covering the entire network. The resulting measures were plotted versus the logarithmic value of the box area instead of the box size length. Multifractal Analysis (MFA) using a box counting algorithm was carried out according to the method of moments ranging from ?

Interpreting participatory Fuzzy Cognitive Maps as complex networks in the social-ecological systems of the Amazonian forests.

EGU General Assembly 2015

Multifractal observations of eddies, oil spills and natural slicks in the ocean surface

Natural and man-made distributions of tensioactive substance concentrations in the sea surface features exhibit self-similarity at all radar reflectivity levels when illuminated by SAR. This allows the investigation of the traces produced by vortices and other features in the ocean surface. The man-made oil spills besides often presenting some linear axis of the pollutant concentration produced by moving ships also show their artificial production in the sea surface by the reduced range of scales, which widens as time measured in terms of the local eddy diffusivity distorts the shape of the oil spills. Thanks to this, multifractal analysis of the different backscattered intensity levels in SAR imagery can be used to distinguish between natural and man-made sea surface features due to their distinct self-similar properties. The differences are detected using the multifractal box-counting algorithm on different sets of SAR images giving also information on the age of the spills. Different multifractal algorithms are compared presenting the differences in scaling as a function of some physical generating process such as the locality or the spectral energy cascade

Multiscale Soil Investigations: Physical Concepts And Mathematical Techniques

Soil variability has often been considered to be composed of “functional” (explained) variations plus random fl uctuations or noise. However, the distinction between these two components is scale dependent because increasing the scale of observation almost always reveals structure in the noise (Burrough, 1983). Soils can be seen as the result of spatial variation operating over several scales, indicating that factors infl uencing spatial variability differ with scale. Th is observation points to variability as a key soil attribute that should be studied

Influence of thresholding in mass and entropy dimension of 3-D soil images

With the advent of modern non-destructive tomography techniques, there have been many attempts to analyze 3-D pore space features mainly concentrating on soil structure. This analysis opens a challenging opportunity to develop techniques for quantifying and describe pore space properties, one of them being fractal analysis. <br><br> Undisturbed soil samples were collected from four horizons of Brazilian soil and 3-D images at 45 μm resolution. Four different threshold criteria were used to transform computed tomography (CT) grey-scale imagery into binary imagery (pore/solid) to estimate their mass fractal dimension (<I>D<sub>m</sub></I>) and entropy dimension (<I>D</I><sub>1</sub>). Each threshold criteria had a direct influence on the porosity obtained, varying from 8 to 24% in one of the samples, and on the fractal dimensions. Linear scaling was observed over all the cube sizes, however depending on the range of cube sizes used in the analysis, <I>D<sub>m</sub></I> could vary from 3.00 to 2.20, realizing that the threshold influenced mainly the scaling in the smallest cubes (length of size from 1 to 16 voxels). <br><br> <I>D<sub>m</sub></I> and <I>D</I><sub>1</sub> showed a logarithmic relation with the apparent porosity in the image, however, the increase of both values respect to porosity defined a characteristic feature for each horizon that can be related to soil texture and depth

Topology of a percolating soil pore network.

A connectivity function defined by the 3D-Euler number, is a topological indicator and can be related to hydraulic properties (Vogel and Roth, 2001). This study aims to develop connectivity Euler indexes as indicators of the ability of soils for fluid percolation. The starting point was a 3D grey image acquired by X-ray computed tomography of a soil at bulk density of 1.2 mg cm-3. This image was used in the simulation of 40000 particles following a directed random walk algorithms with 7 binarization thresholds. These data consisted of 7 files containing the simulated end points of the 40000 random walks, obtained in Ruiz-Ramos et al. (2010). MATLAB software was used for computing the frequency matrix of the number of particles arriving at every end point of the random walks and their 3D representation

Fractal Metrology for biogeosystems analysis

The solid-pore distribution pattern plays an important role in soil functioning being related with the main physical, chemical and biological multiscale and multitemporal processes of this complex system. In the present research, we studied the aggregation process as self-organizing and operating near a critical point. The structural pattern is extracted from the digital images of three soils (<i>Chernozem, Solonetz</i> and <i>"Chocolate" Clay</i>) and compared in terms of roughness of the gray-intensity distribution quantified by several measurement techniques. Special attention was paid to the uncertainty of each of them measured in terms of standard deviation. Some of the applied methods are known as classical in the fractal context (box-counting, rescaling-range and wavelets analyses, etc.) while the others have been recently developed by our Group. The combination of these techniques, coming from Fractal Geometry, Metrology, Informatics, Probability Theory and Statistics is termed in this paper <i>Fractal Metrology</i> (FM). We show the usefulness of FM for complex systems analysis through a case study of the soil's physical and chemical degradation applying the selected toolbox to describe and compare the structural attributes of three porous media with contrasting structure but similar clay mineralogy dominated by montmorillonites