Numerical modeling of the effect of variation of boundary conditions on vadose zone hydraulic properties

An accurate estimation of hydraulic fluxes in the vadose zone is essential for the prediction of water, nutrient and contaminant transport in natural systems. The objective of this study was to simulate the effect of variation of boundary conditions on the estimation of hydraulic properties (i.e. water content, effective unsaturated hydraulic conductivity and hydraulic flux) in a one-dimensional unsaturated flow model domain. Unsaturated one-dimensional vertical water flow was simulated in a pure phase clay loam profile and in clay loam interlayered with silt loam distributed according to the third iteration of the Cantor Bar fractal object Simulations were performed using the numerical model Hydrus 1D. The upper and lower pressure heads were varied around average values of -55 cm for the near-saturation range. This resulted in combinations for the upper and lower constant head boundary conditions, respectively, of -50 and -60 cm, -40 and -70 cm, -30 and -80 cm, -20 and -90 cm, and -10 and -100 cm. For the drier range the average head between the upper and lower boundary conditions was set to -550 cm, resulting in the combinations -500 and -600 cm, -400 and -700 cm, -300 and -800 cm, -200 and -900 cm, and -100 and -1,000 cm, for upper and lower boundary conditions, respectively. There was an increase in water contents, fluxes and hydraulic conductivities with the increase in head difference between boundary conditions. Variation in boundary conditions in the pure phase and interlayered one-dimensional profiles caused significant deviations in fluxes, water contents and hydraulic conductivities compared to the simplest case (a head difference between the upper and lower constant head boundaries of 10 cm in the wetter range and 100 cm in the drier range).

Determination of hydraulic properties of a tropical soil of Hawaii using column experiments and inverse modeling

A method for determining soil hydraulic properties of a weathered tropical soil (Oxisol) using a medium-sized column with undisturbed soil is presented. The method was used to determine fitting parameters of the water retention curve and hydraulic conductivity functions of a soil column in support of a pesticide leaching study. The soil column was extracted from a continuously-used research plot in Central Oahu (Hawaii, USA) and its internal structure was examined by computed tomography. The experiment was based on tension infiltration into the soil column with free outflow at the lower end. Water flow through the soil core was mathematically modeled using a computer code that numerically solves the one-dimensional Richards equation. Measured soil hydraulic parameters were used for direct simulation, and the retention and soil hydraulic parameters were estimated by inverse modeling. The inverse modeling produced very good agreement between model outputs and measured flux and pressure head data for the relatively homogeneous column. The moisture content at a given pressure from the retention curve measured directly in small soil samples was lower than that obtained through parameter optimization based on experiments using a medium-sized undisturbed soil column.

Hydraulic conductivity and water retention in leptosols-regosols and saprolite derived from sandstone, Brazil

Leptosols and Regosols are soils with a series of restrictions for use, mainly related to the effective depth, which have been poorly studied in Brazil. These soils, when derived from sedimentary rocks should be treated with particular care to avoid environmental damage such as aquifer contamination. The purpose of this study was to verify the behavior of hydraulic conductivity and water retention capacity in profiles of Leptosols and Regosols derived from sandstone of the Caturrita formation in Rio Grande do Sul state. The morphology, particle size distribution, porosity, soil density (Ds), saturated hydraulic conductivity (Ks), basic water infiltration in the field (BI) and water retention were determined in soil and saprolite samples of six soil profiles. High Ds, low macroporosity and high microporosity were observed in the profiles, resulting in a low Ks and BI, even under conditions of sandy texture and a highly fractured saprolite layer. The variation coefficients of data of Ks and BI were high among the studied profiles and between replications of a same profile. Water retention of the studied soils was higher in Cr layers than in the A horizons and the volume of plant-available water greater and variable among A horizons and Cr layers.

Transition to parenthood: The role of social interaction and endogenous networks

Empirical studies indicate that the transition to parenthood is influenced by an individual's peer group. To study the mechanisms creating interdepen- dencies across individuals' transition to parenthood and its timing we apply an agent-based simulation model. We build a one-sex model and provide agents with three different characteristics regarding age, intended education and parity. Agents endogenously form their network based on social closeness. Network members then may influence the agents' transition to higher parity levels. Our numerical simulations indicate that accounting for social inter- actions can explain the shift of first-birth probabilities in Austria over the period 1984 to 2004. Moreover, we apply our model to forecast age-specific fertility rates up to 2016.

Hydraulic conductivity and soil-sewage sludge interactions

One of the main problems faced by humanity is pollution caused by residues resulting from the production and use of goods, e.g, sewage sludge. Among the various alternatives for its disposal, the agricultural use seems promising. The purpose of this study was to evaluate the hydraulic conductivity and interaction of soil with sandy-silty texture, classified as Spodosols, from the Experimental Station Itapirema - IPA, in Goiana, state of Pernambuco, in mixtures with sewage sludge from the Mangueira Sewage Treatment Station, in the city of Recife, Pernambuco at rates of 25, 50 and 75 Mg ha-1. Tests were conducted to let water percolate the natural saturated soil and soil-sludge mixtures to characterize their physical, chemical, and microstructural properties as well as hydraulic conductivity. Statistical data analysis showed that the presence of sewage sludge in soils leads to an increase of the < 0.005 mm fraction, reduction in real specific weight and variation in optimum moisture content from 11.60 to 12.90 % and apparent specific dry weight from 17.10 and 17.50 kN m-3. In the sludge-soil mixture, the quartz grains were covered by sludge and filling of the empty soil macropores between grains. There were changes in the chemical characteristics of soil and effluent due to sewage sludge addition and a small decrease in hydraulic conductivity. The results indicate the possibility that soil acidity influenced the concentrations of the elements found in the leachate, showing higher levels at higher sludge doses. It can be concluded that the leaching degree of potentially toxic elements from the sewage sludge treatments does not harm the environment.

Modelling non-industrial private forest landowners' strategic decision making by using logistic regression and neural networks: case of predicting the choice of forest taxation basis

Summary

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Stabilization of soil hydraulic properties under a long term no-till system

The area under the no-tillage system (NT) has been increasing over the last few years. Some authors indicate that stabilization of soil physical properties is reached after some years under NT while other authors debate this. The objective of this study was to determine the effect of the last crop in the rotation sequence (1st year: maize, 2nd year: soybean, 3rd year: wheat/soybean) on soil pore configuration and hydraulic properties in two different soils (site 1: loam, site 2: sandy loam) from the Argentinean Pampas region under long-term NT treatments in order to determine if stabilization of soil physical properties is reached apart from a specific time in the crop sequence. In addition, we compared two procedures for evaluating water-conducting macroporosities, and evaluated the efficiency of the pedotransfer function ROSETTA in estimating the parameters of the van Genuchten-Mualem (VGM) model in these soils. Soil pore configuration and hydraulic properties were not stable and changed according to the crop sequence and the last crop grown in both sites. For both sites, saturated hydraulic conductivity, K0, water-conducting macroporosity, εma, and flow-weighted mean pore radius, R0ma, increased from the 1st to the 2nd year of the crop sequence, and this was attributed to the creation of water-conducting macropores by the maize roots. The VGM model adequately described the water retention curve (WRC) for these soils, but not the hydraulic conductivity (K) vs tension (h) curve. The ROSETTA function failed in the estimation of these parameters. In summary, mean values of K0 ranged from 0.74 to 3.88 cm h-1. In studies on NT effects on soil physical properties, the crop effect must be considered.

Communication in networks with hierarchical branching

We present a simple model of communication in networks with hierarchical branching. We analyze the behavior of the model from the viewpoint of critical systems under different situations. For certain values of the parameters, a continuous phase transition between a sparse and a congested regime is observed and accurately described by an order parameter and the power spectra. At the critical point the behavior of the model is totally independent of the number of hierarchical levels. Also scaling properties are observed when the size of the system varies. The presence of noise in the communication is shown to break the transition. The analytical results are a useful guide to forecasting the main features of real networks.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Absence of epidemic threshold in scale free networks with degree correlations

Random scale-free networks have the peculiar property of being prone to the spreading of infections. Here we provide for the susceptible-infected-susceptible model an exact result showing that a scale-free degree distribution with diverging second moment is a sufficient condition to have null epidemic threshold in unstructured networks with either assortative or disassortative mixing. Degree correlations result therefore irrelevant for the epidemic spreading picture in these scale-free networks. The present result is related to the divergence of the average nearest neighbors degree, enforced by the degree detailed balance condition.

Physical-hydraulic properties of a sandy loam typic paleudalf soil under organic cultivation of 'montenegrina' mandarin (Citrus deliciosa Tenore)¹

Citrus plants are the most important fruit species in the world, with emphasis to oranges, mandarins and lemons. In Rio Grande do Sul, Brazil, most fruit production is found on small properties under organic cultivation. Soil compaction is one of the factors limiting production and due to the fixed row placement of this crop, compaction can arise in various manners in the interrows of the orchard. The aim of this study was to evaluate soil physical properties and water infiltration capacity in response to interrow management in an orchard of mandarin (Citrus deliciosa Tenore 'Montenegrina') under organic cultivation. Interrow management was performed through harrowing, logs in em "V", mowing, and cutting/knocking down plants with a knife roller. Soil physical properties were evaluated in the wheel tracks of the tractor (WT), between the wheel tracks (BWT), and in the area under the line projection of the canopy (CLP), with undisturbed soil samples collected in the 0.00-0.15, 0.15-0.30, 0.30-0.45, and 0.45-0.60 m layers, with four replicates. The soil water infiltration test was performed using the concentric cylinder method, with a maximum time of 90 min for each test. In general, soil analysis showed a variation in the physical-hydraulic properties of the Argissolo Vermelho-Amarelo distrófico arênico (sandy loam Typic Paleudalf) in the three sampling sites in all layers, regardless of the management procedure in the interrows. Machinery traffic leads to heterogeneity in the soil physical-hydraulic properties in the interrows of the orchard. Soil porosity and bulk density are affected especially in the wheel tracks of the tractor (WT), which causes a reduction in the constant rate of infiltration and in the accumulated infiltration of water in this sampling site. The use of the disk harrow and mower leads to greater harmful effects on the soil, which can interfere with mandarin production.

Synchronization reveals topological scales in complex networks

We study the relationship between topological scales and dynamic time scales in complex networks. The analysis is based on the full dynamics towards synchronization of a system of coupled oscillators. In the synchronization process, modular structures corresponding to well-defined communities of nodes emerge in different time scales, ordered in a hierarchical way. The analysis also provides a useful connection between synchronization dynamics, complex networks topology, and spectral graph analysis.

Self-similarity of complex networks and hidden metric spaces

We demonstrate that the self-similarity of some scale-free networks with respect to a simple degree-thresholding renormalization scheme finds a natural interpretation in the assumption that network nodes exist in hidden metric spaces. Clustering, i.e., cycles of length three, plays a crucial role in this framework as a topological reflection of the triangle inequality in the hidden geometry. We prove that a class of hidden variable models with underlying metric spaces are able to accurately reproduce the self-similarity properties that we measured in the real networks. Our findings indicate that hidden geometries underlying these real networks are a plausible explanation for their observed topologies and, in particular, for their self-similarity with respect to the degree-based renormalization.