Spatial Structure Variability Research Articles

The spatial scale of somatostatin subnetworks increases from sensory to association cortex.

Incoming signals interact with rich, ongoing population activity dynamics in cortical circuits. These intrinsic dynamics are the consequence of interactions among local excitatory and inhibitory neurons and affect inter-region communication and information coding. It is unclear whether specializations in the patterns of interactions among excitatory and inhibitory neurons underlie systematic differences in activity dynamics across the cortex. Here, in mice, we compare the functional interactions among somatostatin (SOM)-expressing inhibitory interneurons and the rest of the neural population in auditory cortex (AC), a sensory region of the cortex, and posterior parietal cortex (PPC), an association region. The spatial structure of shared variability among SOM and non-SOM neurons differs across regions: correlations decay rapidly with distance in AC but not in PPC. However, in both regions, activity of SOM neurons is more highly correlated than non-SOM neurons' activity. Our results imply both generalization and specialization in the functional structure of inhibitory subnetworks across the cortex.

Characterization of Bacterial Differences Induced by Cleft-Palate-Related Spatial Heterogeneity.

Background: Cleft palate (CP) patients have a higher prevalence of oral and respiratory tract bacterial infections than the general population. Nevertheless, characteristics of bacterial differences induced by CP-related anatomical heterogeneity are unknown. Methods: In this study, we systematically described the characteristics of bacteria in the oral and nasal niches in healthy children, CP children, healthy adolescents, CP adolescents, and postoperative adolescents by 454-pyrosequencing technology (V3–V6) to determine bacterial differences induced by CP. Results: Due to the CP-induced variations in spatial structure, the early establishment of microecology in CP children was different from that in healthy children. Nasal bacterial composition showed greater changes than in the saliva. Moreover, such discrepancy also appeared in CP and postoperative adolescents who had even undergone surgery > 10 years previously. Interestingly, we found by Lefse analysis that part of bacterial biomarkers in the nasal cavity of CP subjects was common oral flora, suggesting bacterial translocation between the oral and nasal niches. Therefore, we defined the oral–nasal translocation bacteria as O-N bac. By comparing multiple groups, we took the intersection sets of O-N bacs selected from CP children, CP adolescents, and postoperative adolescents as TS O-N bacs with time–character, including Streptococcus, Gemella, Alloprevotella, Neisseria, Rothia, Actinomyces, and Veillonella. These bacteria were at the core of the nasal bacterial network in CP subjects, and some were related to infectious diseases. Conclusions: CP would lead to significant and long-term differences in oral and nasal flora. TS O-N bacs migrating from the oral to the nasal might be the key stone causing nasal flora dysbiosis in the CP patients.

Synchronization of Wave Flows of Arterial and Venous Blood with Phases of the Cardiac Cycle in Norm: Part 5

The muscular-fibrous frame of the heart (MFFH) synchronizes and compensates for the pressor effect of the myocardium between the high/low-energy regions (left/right) of the heart. The anatomical structures of MFFH (plastic muscular-fibrous formation with a phase change in contours, valves, valve rings) form the "cardiac mean integral pressure" (CMIP). MFFH, a variable spatial structure, forms the starting pressor levels of “opening/closing” of valves and hemodynamic vectors of the heart chambers, systemic and pulmonary circulation, and also compensates for excess pressor pressure (having a variable gradient) at the boundaries of the heart chambers. Throughout the cardiac cycle (CC), on the path "venous block-lung-arterial block-aorta," variable pressure values, compensated by the structures of MFFH, are formed between the blood flows of the right and left parts of the heart. A mutual adaptation of SVs of the ventricles is formed by phase-by-phase compensatory plastics of MFFH. CMIP of MFFH is an integral indicator, where each point reflects: 1) CC phase (time and place); 2) the average value of the range of values in which the equilibrium point of pressor compensation between the high/low-energy processes of this CC phase is located. CMIP is a vector of the MFFH phase dynamics, which compensates for the excess pressor effect with a changing gradient through CC.

Spatially variable soils affecting geotechnical strip foundation design

Natural soil variability is a well-known issue in geotechnical design, although not frequently managed in practice. When subsoil must be characterized in terms of mechanical properties for infrastructure design, random finite element method (RFEM) can be effectively adopted for shallow foundation design to gain a twofold purpose: (1) understanding how much the bearing capacity is affected by the spatial variability structure of soils, and (2) optimisation of the foundation dimension (i.e. width B ). The present study focuses on calculating the bearing capacity of shallow foundations by RFEM in terms of undrained and drained conditions. The spatial variability structure of soil is characterized by the autocorrelation function and the scale of fluctuation ( δ ). The latter has been derived by geostatistical tools such as the ordinary Kriging (OK) approach based on 182 cone penetration tests (CPTs) performed in the alluvial plain in Bologna Province, Italy. Results show that the increase of the B / δ ratio not only reduces the bearing capacity uncertainty but also increases its mean value under drained conditions. Conversely, under the undrained condition, the autocorrelation function strongly affects the mean values of bearing capacity. Therefore, the authors advise caution when selecting the autocorrelation function model for describing the soil spatial variability structure and point out that undrained conditions are more affected by soil variability compared to the drained ones. • RFEM is able to consider the soil variability structure in strip foundation design. • Bearing capacity mean value is affected by soil spatial variability. • Bearing capacity is affected by autocorrelation function model. • Coefficient of variation COV reduces as the foundation size increases. • Both drained and undrained soil conditions show similar COV reduction.

Characterizing and Mitigating Intraday Variability: Reconstructing Source Structure in Accreting Black Holes with mm-VLBI

The extraordinary physical resolution afforded by the Event Horizon Telescope has opened a window onto the astrophysical phenomena unfolding on horizon scales in two known black holes, M87* and Sgr A*. However, with this leap in resolution has come a new set of practical complications. Sgr A* exhibits intraday variability that violates the assumptions underlying Earth aperture synthesis, limiting traditional image reconstruction methods to short timescales and data sets with very sparse (u, v) coverage. We present a new set of tools to detect and mitigate this variability. We develop a data-driven, model-agnostic procedure to detect and characterize the spatial structure of intraday variability. This method is calibrated against a large set of mock data sets, producing an empirical estimator of the spatial power spectrum of the brightness fluctuations. We present a novel Bayesian noise modeling algorithm that simultaneously reconstructs an average image and statistical measure of the fluctuations about it using a parameterized form for the excess variance in the complex visibilities not otherwise explained by the statistical errors. These methods are validated using a variety of simulated data, including general relativistic magnetohydrodynamic simulations appropriate for Sgr A* and M87*. We find that the reconstructed source structure and variability are robust to changes in the underlying image model. We apply these methods to the 2017 EHT observations of M87*, finding evidence for variability across the EHT observing campaign. The variability mitigation strategies presented are widely applicable to very long baseline interferometry observations of variable sources generally, for which they provide a data-informed averaging procedure and natural characterization of inter-epoch image consistency.

Spatial heterogeneity in rain-bearing winds, seasonality and rainfall variability in southern Africa's winter rainfall zone

Abstract. A renewed focus on southern Africa's winter rainfall zone (WRZ) following the Day Zero drought and water crisis has not shed much light on the spatial patterns of its rainfall variability and climatological seasonality. However, such understanding remains essential in studying past and potential future climate changes. Using a dense station network covering the region encompassing the WRZ, we study spatial heterogeneity in rainfall seasonality and temporal variability. These spatial patterns are compared to those of rainfall occurring under each ERA5 synoptic-scale wind direction sector. A well-defined “true” WRZ is identified with strong spatial coherence between temporal variability and seasonality not previously reported. The true WRZ is composed of a core and periphery beyond which lies a transition zone to the surrounding year-round rainfall zone (YRZ) and late summer rainfall zone. In places, this transition is highly complex, including where the YRZ extends much further westward along the southern mountains than has previously been reported. The core receives around 80 % of its rainfall with westerly or north-westerly flow compared to only 30 % in the south-western YRZ incursion, where below-average rainfall occurs on days with (usually pre-frontal) north-westerly winds. This spatial pattern corresponds closely to those of rainfall seasonality and temporal variability. Rainfall time series of the core and surroundings are very weakly correlated (R2<0.1), also in the winter half-year, implying that the YRZ is not simply the superposition of summer and winter rainfall zones. In addition to rain-bearing winds, latitude and annual rain day climatology appear to influence the spatial structure of rainfall variability but have little effect on seasonality. Mean annual rainfall in the true WRZ exhibits little association with the identified patterns of seasonality and rainfall variability despite the driest core WRZ stations being an order of magnitude drier than the wettest stations. This is consistent with the general pattern of near homogeneity within the true WRZ, in contrast to steep and complex spatial change outside it.

Problems of Estimating the Resources of Accompanying Elements: A Case Study from the Cu-Ag Rudna Deposit (Legnica-Głogów Copper District, Poland)

As a result of the exploitation of ore deposits, in addition to the main elements, the accompanying elements are also partially recovered. Some of them increase the profitability of exploitation, while others reduce it because they hinder the recovery of the main elements and thus increase the costs of the recovery process. A comprehensive economic calculation to assess the profitability of ore mining depends on an appropriately accurate estimation of the resources of both the main and associated elements. This issue was analyzed with the example of the Cu-Ag Rudna ore deposit (LGCD, Poland). The subject of the assessment was the resources prediction accuracy of the main element (Cu) and four (4) accompanying elements (Co, Ni, Pb, and V) using geostatistical estimation method, in particular the ordinary kriging after the estimation of the relative variograms for describing the spatial variability structures of elements abundance. It was found that the standard kriging errors (deviations) in accompanying elements resources that are scheduled for exploitation within a one-year period in some parts of deposits are drastically greater (2 to 5 times) than the estimation errors of the main element resources. This is due to the sparse sampling pattern for their determinations and/or the high variability (among others nugget effect) of their abundance. In this situation, without additional sampling and a denser sampling pattern, the possibilities of a reliable assessment of the influence of accompanying elements on the economic consequences of exploitation are very limited.

Partitioning macroscale and microscale ecological processes using covariate-driven non-stationary spatial models.

Ecological inference requires integrating information across scales. This integration creates a complex spatial dependence structure that is most accurately represented by fully non-stationary models. However, ecologists rarely use these models because they are difficult to estimate and interpret. Here, we facilitate the use of fully non-stationary models in ecology by improving the interpretability of a recently developed non-stationary model and applying it to improve our understanding of the spatial processes driving lake eutrophication. We reformulated a model that incorporates non-stationary correlation by adding environmental predictors to the covariance function, thereby building on the intuition of mean regression. We created ellipses to visualize how data at a given site correlate with their surroundings (i.e., the range and directionality of underlying spatial processes). We applied this model to describe the spatial dependence structure of variables related to lake eutrophication across two different regions: a Midwestern United States region with highly agricultural landscapes, and a Northeastern United States region with heterogeneous land use. For the Midwest, increases in forest cover increased the homogeneity of the residual spatial structure of total phosphorus, indicating that macroscale processes dominated this nutrient's spatial structure. Conversely, high forest cover and baseflow reduced the spatial homogeneity of chlorophyll a residuals, indicating that microscale processes dominated for chlorophyll a in the Midwest. In the Northeast, increases in urban land use and baseflow decreased the homogeneity of phosphorus concentrations indicating the dominance of microscale processes, but none of our covariates were strongly associated with the residual spatial structure of chlorophyll a. Our model showed that the spatial dependence structure of environmental response variables shifts across space. It also helped to explain this structure using ecologically relevant covariates from different scales whose effects can be interpreted intuitively. This provided novel insight into the processes that lead to eutrophication, a complex and pervasive environmental issue.

Novel Tools for Adjusting Spatial Variability in the Early Sugarcane Breeding Stage.

The detection of spatial variability in field trials has great potential for accelerating plant breeding progress due to the possibility of better controlling non-genetic variation. Therefore, we aimed to evaluate a digital soil mapping approach and a high-density soil sampling procedure for identifying and adjusting spatial dependence in the early sugarcane breeding stage. Two experiments were conducted in regions with different soil classifications. High-density sampling of soil physical and chemical properties was performed in a regular grid to investigate the structure of spatial variability. Soil apparent electrical conductivity (ECa) was measured in both experimental areas with an EM38-MK2® sensor. In addition, principal component analysis (PCA) was employed to reduce the dimensionality of the physical and chemical soil data sets. After conducting the PCA and obtaining different thematic maps, we determined each experimental plot’s exact position within the field. Tons of cane per hectare (TCH) data for each experiment were obtained and analyzed using mixed linear models. When environmental covariates were considered, a previous forward model selection step was applied to incorporate the variables. The PCA based on high-density soil sampling data captured part of the total variability in the data for Experimental Area 1 and was suggested to be an efficient index to be incorporated as a covariate in the statistical model, reducing the experimental error (residual variation coefficient, CVe). When incorporated into the different statistical models, the ECa information increased the selection accuracy of the experimental genotypes. Therefore, we demonstrate that the genetic parameter increased when both approaches (spatial analysis and environmental covariates) were employed.

Interaction of Urban Rivers and Green Space Morphology to Mitigate the Urban Heat Island Effect: Case-Based Comparative Analysis.

The spatial morphology of waterfront green spaces helps generate cooling effects to mitigate the urban heat island effect (UHI) in metropolis cities. To explore the contribution and influence of multi-dimensional spatial indices on the mitigation of UHIs, the green space of the riparian buffer along 18 river channels in Shanghai was considered as a case study. The spatial distribution data of the land surface temperature (LST) in the study area were obtained by using remote sensing images. By selecting the related spatial structure morphological factors of the waterfront green space as the quantitative description index, the growth regression tree model (BRT) was adapted to analyze the contribution of various indexes of the waterfront green space on the distribution of the LST and the marginal effect of blue–green synergistic cooling. In addition, mathematical statistical analysis and spatial analysis methods were used to study the influence of the morphological group (MG) types of riparian green spaces with different morphological characteristics on the LST. The results showed that in terms of the spatial structure variables between blue and green spaces, the contribution of river widths larger than 30 m was more notable in decreasing the LST. In the case of a larger river width, the marginal effect of synergistic cooling could be observed in farther regions. The green space that had the highest connectivity degree and was located in the leeward direction of the river exhibited the lowest LST. In terms of the spatial morphology, the fractional cover values of the vegetation (Fv) and area (A) of the green space were the main factors affecting the cooling effect of the green space. For all MG types, a large green patch that had a high green coverage and connectivity degree, as well as was distributed in the leeward direction of the river, corresponded to the lowest LST. The research presented herein can provide methods and development suggestions for optimizing spatial thermal comfort in climate adaptive cities.

Growth and structural changes in Swiss uneven-aged forests over 100 years, and comparisons between 15 uneven-aged forest types of Europe, North America and Australia

Abstract Long-term changes in growth, physiology and stand structure, as well as differences due to species composition, were examined in six types of Swiss plenter forests: (1) Picea abies dominated, (2) mixtures of P. abies and Abies alba, (3) mixtures of P. abies, A. alba and Fagus sylvatica, (4) mixtures of Larix decidua, Pinus cembra/sylvatica and P. abies, (5) F. sylvatica dominated and (6) species diverse stands. Long-term changes were examined using 19 plots measured for an average of 94 years. These data were then combined with the literature plenter forest data to compare the growth and structure of 15 species compositions from Europe, North America and Australia. Over the past 100 years, common trends included increases in growth, maximum tree sizes, species diversity, and increasingly convex diameter distributions with fewer medium sized trees and more very large trees. Differences in growth between species compositions were generally consistent with their light-use efficiency, transpiration and water-use efficiency, which were calculated using a process-based model. Stand volume of each forest type was negatively correlated with the shade tolerance (but not crown architectures) of the species it contained, but no such correlations existed for stand basal area. This reflects the high structural variability of these forests (through time and between sites) as well as the high tree neighbourhood variability within plots, which appear to buffer the effects of stand density on regeneration and recruitment. This study shows that despite significant temporal and spatial variability in structure, the negative exponentially shaped diameter distributions were maintained in the long term by applying simple silvicultural principles rather than requiring a very specific stand structure, i.e. a certain stand density, or that all diameter classes strictly lie on the curve of a negative exponentially shaped diameter distribution. The values of stand structural characteristics reported in this study could be used as a guide for the conversion of relatively simply structured forests into plenter forests.

A genetic algorithm for resizing and sampling reduction of non-stationary soil chemical attributes optimizing spatial prediction

Aim of study: To evaluate the influence of the parameters of the geostatistical model and the initial sample configuration used in the optimization process; and to propose and evaluate the resizing of a sample configuration, reducing its sample size, for simulated data and for the study of the spatial variability of soil chemical attributes under a non-stationary with drift process from a commercial soybean cultivation area.Area of study: Cascavel, BrazilMaterial and methods: For both, the simulated data and the soil chemical attributes, the Genetic Algorithm was used for sample resizing, maximizing the overall accuracy measure.Main results: The results obtained from the simulated data showed that the practical range did not influence in a relevant way the optimization process. Moreover, the local variations, such as variance or sampling errors (nugget effect), had a direct relationship with the reduction of the sample size, mainly for the smaller nugget effect. For the soil chemical attributes, the Genetic Algorithm was efficient in resizing the sampling configuration, since it generated sampling configurations with 30 to 35 points, corresponding to 29.41% to 34.31% of the initial configuration, respectively. In addition, comparing the optimized and initial configurations, similarities were obtained regarding spatial dependence structure and characterization of spatial variability of soil chemical attributes in the study area.Research highlights: The optimization process showed that it is possible to reduce the sample size, allowing for lesser financial investments with data collection and laboratory analysis of soil samples in future experiments.

Spatial variability of soil mineral fractions and bulk density in Northern Ireland: Assessing the influence of topography using different interpolation methods and fractal analysis

Understanding how topography affects the distribution of soil properties is essential in the management of landscape hydrology and establishment of sustainable soil management practices. This study investigated the impact of topography on the variation in particle size distribution, coarse fragments, and soil bulk density using different interpolation techniques and fractal analysis. It also evaluated the performance of various interpolation techniques in predicting and characterizing the distribution of soil properties. The study was conducted using data from 620 samples extracted from the Tellus and LUCAS databases in Eglinton and Castlederg counties, Northern Ireland. Terrain attributes were obtained at a 30 × 30 m resolution using a global digital elevation model (GDEM) reintroduced to the Universal Transverse Mercator (UTM) projection. Interpolation analyses were conducted using inverse distance weighting (IDW), ordinary kriging (OK), block kriging (BK) and co-kriging (CK). Among the terrain attributes, elevation was the most influential covariate for CK. In addition, fractal analysis was conducted to assess the self-similarity of the soil properties. Prediction accuracy of the interpolation methods was evaluated using the Nash-Sutcliffe efficiency, mean absolute error, index of agreement, and Pearson correlation coefficient. Spatial maps produced from the kriging techniques showed high accuracy in the prediction of soil particle size distribution and bulk density. The use of elevation as an auxiliary variable was successful in producing accurate soil property distribution maps with CK. The fractal parameters showed that the soil properties had short range spatial variability, anti-persistent nature, and strong spatial structure. Additionally, the fractal dimension was strongly correlated with sand, silt and clay contents and bulk density, and weakly correlated with the coarse fragments.

Mechanisms of Decadal North Atlantic Climate Variability and Implications for the Recent Cold Anomaly

AbstractDecadal sea surface temperature (SST) fluctuations in the North Atlantic Ocean influence climate over adjacent land areas and are a major source of skill in climate predictions. However, the mechanisms underlying decadal SST variability remain to be fully understood. This study isolates the mechanisms driving North Atlantic SST variability on decadal time scales using low-frequency component analysis, which identifies the spatial and temporal structure of low-frequency variability. Based on observations, large ensemble historical simulations, and preindustrial control simulations, we identify a decadal mode of atmosphere–ocean variability in the North Atlantic with a dominant time scale of 13–18 years. Large-scale atmospheric circulation anomalies drive SST anomalies both through contemporaneous air–sea heat fluxes and through delayed ocean circulation changes, the latter involving both the meridional overturning circulation and the horizontal gyre circulation. The decadal SST anomalies alter the atmospheric meridional temperature gradient, leading to a reversal of the initial atmospheric circulation anomaly. The time scale of variability is consistent with westward propagation of baroclinic Rossby waves across the subtropical North Atlantic. The temporal development and spatial pattern of observed decadal SST variability are consistent with the recent observed cooling in the subpolar North Atlantic. This suggests that the recent cold anomaly in the subpolar North Atlantic is, in part, a result of decadal SST variability.

РЕГИОНАЛЬНЫЕ ОСОБЕННОСТИ ГЕОМОРФОЛОГИЧЕСКОГО СТРОЕНИЯ ДРЕВНЕЛЕДНИКОВОЙ ОБЛАСТИ В ЦЕНТРЕ РУССКОЙ РАВНИНЫ

A generalization of the materials of a comprehensive paleogeographic study in the ancient glacial region of the Russian Plain is aimed at establishing regional features of the geomorphological structure and patterns of spatial variability of indicators. For these purposes, geological and geomorphological zoning of the territory was carried out on the basis of a systematic approach and under the control of paleogeographic expertise. On the compiled map, the territorial divisions are highlighted: against the background of different age paleogeographic zones, 4 geological provinces and 14 geologic-geomorphological regions are distinguished. Their complex characteristics are obtained by the totality of the leading geomorphological processes. Particular attention is paid to the study of the glacial relief, which plays an important role in paleogeographic reconstructions of this area. Marginal zones infrastructure of the ice sheets of different ages has been studied in detail, which makes it possible to make more exact the maximum and stadial boundaries of the Moscow and Kalinin ice sheets. Thus, regional features of the structure and composition of the morpholithogenic basis of landscapes are established. The laws of its formation are confirmed: geological and geomorphological heredity and paleogeographic conditionality. The study of regional features of the geomorphological structure of the ancient glacial region makes a significant contribution to the reconstruction of the glacial rhythm of the Pleistocene. The results of integrated geologic-geomorphological zoning facilitate the regional targeted estimation of the state of geoecological stability of geosystems and make it more justified.

Prestack seismic facies analysis via waveform sparse representations

Seismic facies analysis based on prestack data is becoming popular. Vertical elastic transitions produce the spatial structure variation of prestack waveforms, whereas lateral elastic transitions produce the amplitude intensity variation. In stratigraphic seismic facies analysis, more attention should be paid to waveform spatial structure than amplitude intensity. Conventional classification methods based on distance metrics are difficult to adapt to stratigraphic seismic facies analysis because a distance metric is a comprehensive measure of waveform structure and amplitude intensity. We have developed a dictionary learning (DL) method for prestack seismic facies analysis. The method first learns several dictionaries from labeled prestack waveform data, and these dictionaries consist of several normalization vector bases. The prestack waveform spatial structure is, therefore, embedded in these learned dictionaries, and the amplitude intensity is eliminated by the normalization process. Afterward, these dictionaries are used to sparsely represent prestack seismic data. Seismic facies are classified and determined according to representation error. A source error separation method is used to improve the antinoise performance of DL by iteratively segmenting the noise out in the training data. The results on synthetic and field seismic data show that the proposed method has a stronger tolerance to noise than K singular value decomposition, and the obtained seismic facies boundary is more accurate and clearer than the support vector machine and k-nearest neighbors algorithm. This demonstrates that the proposed method is an effective seismic facies analysis technique.

Geostatistical prediction through convex combination of Archimedean copulas

A common problem in geostatistics is to interpolate a variable at unsampled locations using available data. Kriging has been the conventional method of solving this problem by providing the weighted average of samples, which is determined by minimizing the estimation variance. Kriging variance is a function of the samples’ spatial configuration and the variable’s spatial dependence structure. The latter is described by covariance that reduces complex dependence structures of natural phenomena to a single measure, introducing substantial simplifications. Another issue about kriging is that its variance does not depend on the sample values. Therefore, applying new methods such as spatial copulas that better describe the spatial dependence structure of variables and take advantage of sample values and spatial dependence structure would be helpful. This study compares prediction through the convex combination of Archimedean copulas to kriging using seven variables of the Jura data set. The empirical marginal distribution of variables and fitted kernel density estimates based on the Gaussian, triangular, Epanechnikov and gamma functions were used to investigate the effects of margins on the results. The mixed copulas were capable of describing various types of dependencies with asymmetric upper and lower tails. However, the Gaussian copula failed to explain the spatial dependence structure of variables and had the worst results among the copula-based approaches. The application of empirical marginal distribution of variables has generally given better results than the fitted models. For variables with large ratios of nugget effect to sill, in general, the copula-based approaches showed an advantage over kriging due to better reproduction of the mean values and distributions of the variables, having lower mean squared errors and higher correlation coefficients between the predicted and observed values. On the other hand, for variables with small nugget effects, kriging has better performance regarding all criteria except for the mean value reproduction. This study suggests using a convex combination of Archimedean copulas to predict variables with significant nugget effects.

Multiple-Point Statistical Simulation of rock fracture network as a key control on the hydrogeology and salinity: a case study from the Qarabagh area, West Azarbayjan Province, Iran

Modeling and characterization of the geometry and distribution of Rock Facture Networks (RFNs) are essential in applications such as hydrogeological or environmental evaluations. It is widely accepted that RFNs are potentially associated with the hydrogeological (thus salinity) characteristics of the surrounding environments. Despite the complexity and inaccessibility of RFNs, stochastic methods provide a functional framework to predict their characteristics in the subsurface. An efficient tool for modeling RFNs is the Discrete Fracture Network (DFN) which also includes a number of geostatistical techniques that consider spatial variability structure. The advantages of these techniques are: realistic results, ease of application, and uncertainty assessments. Multiple-point geostatistics/statistics (MPS) is a modern and effective geostatistical tool for realistically simulating RFNs. In the present study, we modeled the RFNs in a location near the Qarabagh area, in the western Urmia Lake; in this regard, we used the Single Normal Equation Simulation (SNESIM) algorithm of the MPS geostatistical method using Training Images (TIs) instead of variograms. The required datasets and information for this modeling was provided using the field measurements of the fracture orientations and dips, as well as the outcrop photographs. The outcomes of these models can be used in predicting the salinity distribution in the surrounding area . Therefore, through the SNESIM algorithm, TIs obtained from the outcrop photographs, and direct measurements, 100 RFN realizations were generated at each station. These realizations were then averaged to predict the locations with higher and lower fracture probabilities and to assess the general trend of the fracture distributions.

Geostatistical-based index for spatial variability in soil properties

ABSTRACT The assessment of spatial variability of environmental variables such as soil properties is important for site-specific management. A geostatistical index that allows quantifying and characterizing the structure of spatial variability is fundamental in this context. Thus, this study aimed to develop a new spatial dependency index, called the Spatial Dependence Measure (SDM) for the spherical, exponential, Gaussian, cubic, pentaspherical, and wave semivariogram models; and comparing it with some of the indexes available in the literature. The SDM is also [...]

Modelling the spatial variability of soil micronutrients for site specific nutrient management in a semi-arid tropical environment

Deficiency of micronutrients in the soils of semi-arid tropical (SAT) environments is a major limiting factor for crop production. This study aims to model the spatial variability of soil micronutrients in croplands of Thimmajipet block, Mahabubnagar, Telangana, India. We collected 1508 georeferenced surface (0–15 cm) soil samples at a grid interval of 325 × 325 m and analysed them for DTPA extractable fractions of iron (Fe), manganese (Mn), copper (Cu), zinc (Zn) and boron (B). Landform and topographic variables were derived from the digital elevation model of the study area. Results showed that all the micronutrients varied highly (CV > 35%) in the order of Zn > Fe > Cu > B > Mn. The Q–Q plots indicated non-normal frequency distribution for all the micronutrients, and log transformation improved them. We compared spherical, circular, exponential, and Gaussian models of ordinary kriging interpolation to define the spatial variability structure. The spherical model with the lowest RMSE and better r2 values was selected to map the spatial distribution of the micronutrients. The nugget to sill ratio (N:S > 0.75) indicated that the micronutrients were weakly spatial dependent. Multiple regression analysis (p < 0.05) indicated that the micronutrient concentration and spatial distribution were controlled by (1) parent material; (2) CaCO3; (3) OC; and (4) antagonistic nutrient interaction. Further, critical deficiency zones of Zn, Cu, B and Fe were delineated by integrating cadastral map on the spatial distribution maps. Our study demonstrates that the large scale spatial variability mapping of soil micronutrients is a prerequisite for implementing site-specific nutrient management in the SAT regions.