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Abstract Background Taiwan is geographically located in a region that spans both tropical and subtropical climates (22–25°N and 120–122°E). The Taiwan Centers for Disease Control have found that the ecological habitat of Aedes aegypti appears only south of 23.5°N. Low temperatures may contribute to this particular habitat distribution of Ae. aegypti under the influence of the East Asian winter monsoon. However, the threshold condition related to critically low temperatures remains unclear because of the lack of large-scale spatial studies. This topic warrants further study, particularly through national entomological surveillance and satellite-derived land surface temperature (LST) data. Methods We hypothesized that the distribution of Ae. aegypti is highly correlated with the threshold nighttime LST and that a critical low LST limits the survival of Ae. aegypti. A mosquito dataset collected from the Taiwan Centers for Disease Control was utilized in conjunction with image data obtained from the moderate resolution imaging spectroradiometer (MODIS) during 2009–2011. Spatial interpolation and phi coefficient methods were used to analyze the correlation between the distributions of immature forms of Ae. aegypti and threshold LST, which was predicted from MODIS calculations for 348 townships in Taiwan. Results According to the evaluation of the correlation between estimated nighttime temperatures and the occurrence of Ae. aegypti, winter had the highest peak phi coefficient, and the corresponding estimated threshold temperatures ranged from 13.7 to 14 °C in the ordinary kriging model, which was the optimal interpolation model in terms of the root mean square error. The mean threshold temperature was determined to be 13.8 °C, which is a critical temperature to limit the occurrence of Ae. aegypti. Conclusions An LST of 13.8 °C was found to be the critical temperature for Ae. aegypti larvae, which results in the near disappearance of Ae. aegypti during winter in the subtropical regions of Taiwan under the influence of the prevailing East Asian winter monsoon.

Abstract Geostatistical methods are one of the advanced techniques used for interpolation of groundwater quality data. The results obtained from geostatistics will be useful for decision makers to adopt suitable remedial measures to protect the quality of groundwater sources. Data used in this study were collected from 78 wells in Varamin plain aquifer located in southeast of Tehran, Iran, in 2013. Ordinary kriging method was used in this study to evaluate groundwater quality parameters. According to what has been mentioned in this paper, seven main quality parameters (i.e. total dissolved solids (TDS), sodium adsorption ratio (SAR), electrical conductivity (EC), sodium (Na+), total hardness (TH), chloride (Cl−) and sulfate (SO4 2−)), have been analyzed and interpreted by statistical and geostatistical methods. After data normalization by Nscore method in WinGslib software, variography as a geostatistical tool to define spatial regression was compiled and experimental variograms were plotted by GS+ software. Then, the best theoretical model was fitted to each variogram based on the minimum RSS. Cross validation method was used to determine the accuracy of the estimated data. Eventually, estimation maps of groundwater quality were prepared in WinGslib software and estimation variance map and estimation error map were presented to evaluate the quality of estimation in each estimated point. Results showed that kriging method is more accurate than the traditional interpolation methods.

Knowledge of the relationship between soil sampling density and spatial autocorrelation with interpolation accuracy allows more time- and cost-efficient spatial analysis. Previous studies produced contradictory observations regarding this relationship, and this study aims to determine and explore under which conditions the interpolation accuracy of chemical soil properties is affected. The study area covered 823.4 ha of agricultural land with 160 soil samples containing phosphorus pentoxide (P2O5) and potassium oxide (K2O) values. The original set was split into eight subsets using a geographically stratified random split method, interpolated using the ordinary kriging (OK) and inverse distance weighted (IDW) methods. OK and IDW achieved similar interpolation accuracy regardless of the soil chemical property and sampling density, contrary to the majority of previous studies which observed the superiority of kriging as a deterministic interpolation method. The primary dependence of interpolation accuracy to soil sampling density was observed, having R2 in the range of 56.5–83.4% for the interpolation accuracy assessment. While this study enables farmers to perform efficient soil sampling according to the desired level of detail, it could also prove useful to professions dependent on field sampling, such as biology, geology, and mining.

There is a number of problems associated with the prediction of the spatial distribution of ecological parameters. In this paper, two similar problems are considered as examples of the application of statistical methods for the analysis of ecological data.The first problem is to quantify the nitrogen status of plants relying on aerial photos. Accurate prediction of plant nutritional needs during the growing season is necessary for efficient use of fertilizers, optimal yields and high quality products. A method of solving this problem is based on the analysis of the optical characteristics of plants in digital images. To improve this method, a module responsible for automatic construction of calibration curves for the quantitative assessment of plant nitrogen status was developed.The second problem is to assess the level of ecological indicators in selected field areas. It is assumed that the initial data are a set of ecological or agro-chemical data measured in situ, as well as an aerial photographic image of the object. This paper proposes approaching this problem by using a combination of the kriging and binary regression methods. The first step is variogram analysis, and then a set of ecological parameter estimates is built by the ordinary kriging method. Next, we set a threshold level for the given zone, introduce a dummy variable that takes the value 1 if the parameter value exceeds the threshold, and 0 otherwise. Thus, we get a basis for a logistic regression where factors include a set of estimates predicted by kriging.The article also presents application examples for these methods.

In this study, some interpolation methods were evaluated for estimating groundwater depth in the Iranshahr- Bampour Plain during 2003, 2007 and 2012 in the months of May and October. Data used belonged to 42- 48 wells scattered across the study area. The methods used contained geostatistical approaches of Ordinary Kriging (OK) and Universal Kriging (UK), and deterministic approaches of Inverse Distance Weighting (IDW), Radial Basis Function (RBF) and Local Polynomial Interpolation (LPI). The performance of the prediction methods was evaluated through cross-validation with comparison criteria of determination coefficient (R2), root mean square error (RMSE) and mean bias error (MBE). The statistical analysis showed a high variance and coefficient of variation of groundwater depth and an increase in the average depth to groundwater during bygone years especially during 2003-2007 period. Directional semivariograms were calculated to find out the drift direction. UK method, with the first and second-order polynomials as drift, and different semivariogram models was examined. According to cross-validation results, the best geostatistical method for estimating groundwater depth was OK (with spherical semivariogram) for 1382 and 1391, and UK with J-bessel semivariogram model and second and first drift orders, respectively, for May and October 2007. Moreover, the cross-validation results indicated that LPI, with RMSE equal to 6.94, 5.87 and 8.65 m, respectively, for May 2003, 2007 and 2012, and 6.86, 6.54 and 8.68 m, respectively, for October 2003, 2007 and 2012 is the best method of interpolation among others. The generated maps of groundwater depth revealed a drop in depth to groundwater; therefore, an occurrence of water crisis over the study region during the recent years. Therefore, it is necessary to consider some management scenarios including exploitation control and alteration of crop pattern and irrigation systems for an optimum use of water resources and achieving a sustainable agriculture across the region.

Investigation on spatial distribution of tree growth characteristics in different forest stands, has a fundamental role in assessing possible harvest planning considering potential of the stands. The aim of this study is mapping the spatial distribution of forest characteristics such as stand volume growth, diameter, growth, ingrowth) and determining the amount of tree mortality in the district three of Sangdeh region within a period of 5 years. Two methods of Ordinary Kriging (OK) and weighted Inverse Distance (IDW) interpolation were applied for mapping. For this purpose, we calculated the increment using direct measurement in 130 permanent sample plots. The results of this study showed that the mean volume increment, diameter increment, ingrowth and annual mortality were 5.65 cubic meters per hectare per year, 0.48 cm, 3.5 and 4.2 stems per hectare per year, respectively. For volume increment, the IDW method by power one and with a root mean square error 0.29 cubic meters per hectare per year, for diameter increment, ingrowth and annual mortality the ordinary Kriging method with a root mean square error 0.219 cm per year, 1.4 and 2.8 nha-1y-1 showed better results, respectively. Overall the results showed that geostatistical methods are efficient methods for mapping forest growth characteristics.

NASA’s ICESat-2 has been providing sea ice freeboard measurements across the polar regions since October 2018. In spite of the outstanding spatial resolution and precision of ICESat-2, the spatial sparsity of the data can be a critical issue for sea ice monitoring. This study employs a geostatistical approach (i.e., ordinary kriging) to characterize the spatial autocorrelation of the ICESat-2 freeboard measurements (ATL10) to estimate weekly freeboard variations in 2019 for the entire Ross Sea area, including where ICESat-2 tracks are not directly available. Three variogram models (exponential, Gaussian, and spherical) are compared in this study. According to the cross-validation results, the kriging-estimated freeboards show correlation coefficients of 0.56–0.57, root mean square error (RMSE) of ~0.12 m, and mean absolute error (MAE) of ~0.07 m with the actual ATL10 freeboard measurements. In addition, the estimated errors of the kriging interpolation are low in autumn and high in winter to spring, and low in southern regions and high in northern regions of the Ross Sea. The effective ranges of the variograms are 5–10 km and the results from the three variogram models do not show significant differences with each other. The southwest (SW) sector of the Ross Sea shows low and consistent freeboard over the entire year because of the frequent opening of wide polynya areas generating new ice in this sector. However, the southeast (SE) sector shows large variations in freeboard, which demonstrates the advection of thick multiyear ice from the Amundsen Sea into the Ross Sea. Thus, this kriging-based interpolation of ICESat-2 freeboard can be used in the future to estimate accurate sea ice production over the Ross Sea by incorporating other remote sensing data.

The occurrence of structural failures in equipment and civil structures accrues from several factors, such as impact, friction and fatigue, requiring efforts for their correction. However, due to safety risks that such failures represents in the industrial environment, it is necessary to monitor the structures and equipment with the aim of quickly identify the beginning of a failure, which allows preventive measures to be taken in a timely manner. Given this scenario, the Ordinary Kriging was used in this work to interpolate numerical data of damage metric based on electromechanical (E/M) impedance in an aluminum plate. The results shows that the method was able to map the failure areas, in addition to evidences preferred directions of structural failure propagation. Through kriging variance, were mapped areas with sensor (PZTs) deficiency, in such a way that such information could provide guidelines for the establishment of sensor grids in order to determine more appropriate arrangements for collecting information form the analyzed structure. From the results obtained it is necessary to carry out additional research with the purpose of improve the method application, understand its limitations and analyze the feasibility of implantation in the industrial environment. [ABSTRACT FROM AUTHOR]

Geostatistical tools have been increasingly applied to compile spatial distribution maps of groundwater quality. Results of geostatistics can be helpful for decision-makers to carry out appropriate remedial actions to sustain the quality of groundwater sources. The main purpose of this paper is to assess the groundwater hydrogeochemistry of Tehran-Karaj plain aquifer, Iran, by a forward geostatistical method and an inverse geostatistical method called sequential Gaussian simulation. Seven important hydrogeochemical properties of the aquifer including total dissolved solids, sodium adsorption ratio, electrical conductivity, sodium, total hardness, chloride, and sulfate were analyzed and compiled geostatistically. Data were taken from 137 well samples in 2016. After data normalization, variography was compiled, and experimental variograms were plotted; then, the best theoretical model was fitted on each variogram based on the minimum residual sum of squares. Cross-validation was used to determine the accuracy of parameters related to the variograms. Estimation maps of the groundwater hydrogeochemistry were prepared, and the estimation variance map was drawn to assess the accuracy of estimation in each estimated point. Forward geostatistical methods are subjected to smoothing, while inverse geostatistical methods are not subjected to this problem. The results of this study revealed that the utilized inverse geostatistical methods called simulation algorithms are more accurate than forward methods. Eventually, estimation maps of each parameter, as well as error maps, were compiled, and critical regions have been proposed according to the simulated maps. [ABSTRACT FROM AUTHOR]

People are increasingly exposed to electromagnetic radiation with the rapid development of technologies such as broadcasting and mobile communication system. There is a concern that long-term exposure at low levels may be associated with various non-specific physical symptoms and ecological effects on animals and plants. It is extremely important to measure and analyze the electromagnetic radiation levels in order to protect people from the possible effects of electromagnetic radiation. A large-scale assessment of the effects of electromagnetic radiation on health or on ecology requires the ambient electromagnetic radiation levels over areas too vast to cover with conventional measurement methods. In this article, detailed information about the measurement tools and measurement method are given. The electromagnetic radiation exposure levels were measured on the main streets in the dense urban areas of Beijing, the capital of China. We apply ordinary kriging as an interpolation technique to assess the electromagnetic radiation exposure in large outdoor areas based on car-mounted measurements along the surrounding roads. The electromagnetic radiation exposure levels for larger areas can be investigated visually on the electromagnetic pollution map, which can assist decision makers by identifying the hotspots. [ABSTRACT FROM AUTHOR]

In this study, the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-El khroub valley has been processed simultaneously with Multivariate analysis, geostatistical modeling, WQI, and geochemical modeling. Cluster analysis identified three main water types based on the major ion contents, where mineralization increased from group 1 to group 3. These groups were confirmed by FA/PCA, which demonstrated that groundwater quality is influenced by geochemical processes (water–rock interaction) and human practice (irrigation). The exponential semivariogram model fitted best for all hydrochemical parameters values and WQI. Groundwater chemistry has a strong spatial structure for Mg, Na, Cl, and NO3, and a moderate spatial structure for EC, Ca, K, HCO3, and SO4. Water quality maps generated using ordinary Kriging are consistent with the HCA and PCA results. All water groups are supersaturated with respect to carbonate minerals, and dissolution of kaolinite and Ca-smectite is one of the processes responsible for hydrochemical evolution in the area. [ABSTRACT FROM AUTHOR]

Spatial interpolation methods are frequently used to characterize soil attributes' spatial variability. However, inconclusive results, about the comparative performance of these methods, have been reported in the literature. Therefore, the present study aimed to analyze the efficiency of ordinary kriging (OK) and inverse distance weighting (IDW) methods in estimating the soil penetration resistance (SPR), soil bulk density (SBD), and soil moisture content (SM) using two distinct sampling grids. The soil sampling was performed on a 5.7 ha area in Southeast Brazil. For data collection, a regular grid with 145 points (20 x 20 m) was created. Soil samples were taken at a 0.20 m layer depth. In order to compare the accuracy of OK and IDW, another grid was created from the initial grid (A), by eliminating one interspersed line, which resulted in a grid with 41 sampled points (40 x 40 m). Results showed that sampling grid A presented less errors than B, proving that the more sampling points, the lower the errors that are associated with both methods will be. Overall, the OK was less biased than IDW only for SBD (A) and SM (B) maps, whereas IDW outperformed OK for the other attributes for both sampling grids. [ABSTRACT FROM AUTHOR]

Geostatistics play an important role for reserve estimation in mining industry. Geostatistical tools became popular because of its high degree of accuracy and time saving process for estimation. The uncertainty of geological deposit can be populated by geo-statistical tools. The limestone ore deposit is studied in this paper. The assay value of individual constituents of limestone ore i.e CaO, SiO2, Al2O3 and Fe2O3 are determined for a block by using inverse square distance weighting (ISDW) method. The average assay value of those individual constituents are 45.85, 15.94, 1.56 and 0.82 percentage respectively. The assay value of CaO is also estimated by two linear method of estimation i.e ISDW and ordinary kriging (OK). The assay value of CaO are determined by 45.85 and 44.67 percentage respectively. The assay values are properly validated and concluded accordingly. The application of ISDW and OK are implemented to build the resource model together in order to assess the uncertainty of the deposit. Grade estimation by using different geo-statistical techniques are done by SURPAC mine planning software. [ABSTRACT FROM AUTHOR]

• The Ordinary Kriging (OK) method can be regarded as an instance of the Radial Basis Function (RBF) interpolation framework. • Four different functions have been implemented in the RBF interpolation framework to carry out core power mapping. • Measurements from DayaBay Unit 1 PWR are used to verify the accuracy of the RBF interpolation framework. Reactor core power mapping can be regarded as an interpolation problem of scattered data which is common in many scientific and engineering fields, and the Radial Basis Function (RBF) method provides an excellent tool for interpolating multidimensional scattered data and thus an appropriate approach for power mapping. By using the dual form of the RBF interpolation expression, this study shows that the Ordinary Kriging (OK) method authors utilized before can be regarded as an instance of RBF framework. Moreover, Gaussian function, multiquadric function, inverse quadratic function and inverse multiquadric function have been implemented in RBF interpolation framework to carry out core power mapping. Measurements from DayaBay Unit 1 PWR are used to verify the accuracies of these four RBF functions, and comparisons are made among these four methods and the OK method. The reconstructed assembly power distribution results and the calculation speeds show that the RBF interpolation framework is suitable for on-line core power distribution monitoring. [ABSTRACT FROM AUTHOR]

The ability of a soil to provide the productivity service depends on the fulfillment of the functions that enable the realization of productivity service (PS). This study was conducted to determine and map the PS capacity of surface and subsurface soils in a 195-ha farmland located at Amasya province of Turkey. Functions that contribute to the provision of PS have been identified, and effective indicators ensuring the realization of functions have been identified. Indicator values were converted to unitless scores using non-linear scoring functions defined in soil management assessment framework. Simple additive (SA) and weighted additive (WA) methods were used to calculate soil functions scores and PS index values. The weights representing the contribution ratio of each indicator to soil functions as well as each function to PS index were obtained by employing the Analytical Hierarchy Process (AHP). Soil functions scores were calculated by summing of the weighted indicator scores, and the PS index value was obtained by summing the weighted function scores. Ordinary kriging, inverse distance weighting and radial basis function methods were used to produce maps for functions and PS index values. Root mean squared error and mean absolute error values were used as criteria to determine the most accurate interpolation method. The AHP technique revealed that nutrient cycle function had the highest (34%) contribution to the provision of PS, while the durability and resistance function (15%) had the lowest contribution. The PS index value was calculated as 0.57 and 0.59 by SA and WA methods, respectively. The PS index values and soil functions, except the resistance and resilience, calculated both by SA and WA were slightly different for surface and sub-surface soils. The results revealed that organic carbon is the most influential indicator affecting the soil functions and consequently the PS of soils. [ABSTRACT FROM AUTHOR]

659 water samples from springs and wells in the Sabatini and Vicano-Cimino Volcanic Districts (central Italy) were analyzed for arsenic (As), fluoride (F−) and radon (222Rn) concentrations. Waters mostly sourced from a shallow and cold aquifer hosted within volcanic rocks, which represents the main public drinking water supply. Cold waters from perched aquifers within sedimentary formations and thermal waters related to a deep hydrothermal reservoir were also analyzed. The highest concentrations of As and F− were measured in the thermal waters and attributed to their enhanced mobility during water-rock interaction processes at hydrothermal temperatures. Relatively high concentrations of As and F− were also recorded in those springs and wells discharging from the volcanic aquifer, whereas waters hosted in the sedimentary units showed significantly lower contents. About 60% (As) and 25% (F−) of cold waters from the volcanic aquifer exceeded the maximum allowable concentrations for human consumption. Such anomalously high levels of geogenic pollutants were caused by mixing with fluids upwelling through faulted zones from the hydrothermal reservoir. Chemical weathering of volcanic rocks and groundwater flow path were also considered to contribute to the observed concentrations. Cold waters from the volcanic aquifer showed the highest 222Rn concentrations, resulting from the high contents of Rn-generating radionuclides in the volcanic units. Approximately 22% of these waters exceeded the recommended value for human consumption. A specific Quality Index (QI), comprised between 1 (very low) and 4 (very high), was computed for each water on the basis of As, F− and 222Rn concentrations and visualized through a spatial distribution map processed by means of geostatistical techniques. This map and the specific As, F− and 222Rn maps can be regarded as useful tools for water management by local authorities to both improve intervention plans in contaminated sectors and identify new water resources suitable for human consumption. Image 1 • Volcanic-sedimentary aquifers from Central Italy were analyzed for As, F− and 222Rn. • Water-rock interaction and input of thermal fluids are sources of geogenic pollutants. • High contents of geogenic pollutants were pertaining to the volcanic aquifers. • Quality Indexes (QIs) were obtained by combining the As, F− and 222Rn concentrations. • QI distribution maps can be used for water management in geogenically polluted areas. Spatial distribution maps of geogenic pollutants (As, F− and 222Rn) and quality indexes for water resources were determined in volcanic-sedimentary aquifers of central Italy for water management. [ABSTRACT FROM AUTHOR]

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Aims: Deficit irrigation might be a remedy to increase water use efficiency in water scarce areas albeit it may cause to: a) increase salt accumulation in the root-zone, b) decrease crop yield. Therefore, monitoring and assessment of salt accumulation in the root-zone is necessary in deficit irrigation practices. Primary objectives of this work were to: a) assess salt accumulation in the root-zone of tomato crop irrigated with conventional deficit irrigation (DI-50) through using ordinary kriging interpolation technique, and b) compare it with full irrigation (FI) treatment. Methods and Results: To this end, soil electrical conductivity (EC in dS m- 1) measurements were conducted under emitters, between emitters and plant, and under plant on right and left side of root-zone by using an EC probe. In order to assess spatial and temporal changes of salt accumulation in the root-zone of tomato crop, EC lectures were done: a) at the beginning crop growth stage, b) in the middle, and c) at the end of growing season. In order to generate salinity maps in the root-zone, geostatistical interpolation techniques have been utilized. Geostatistical analysis has been realized by using "Jeostat-2017" software. Geostatistical analysis results indicated that the most suitable theoretical semivariogram model to the experimental semivariogram was Gaussian and/or Spherical model. Cross validation analysis revealed that kriging interpolation errors were fitted to the normal distribution, indicating that theoretical semivariogram model and its parameters as well as kriging search parameters are representative for the study site. Kriging errors helped us to evaluate efficiency of sampling design for salinity assessment. Conclusions: In this regard, results showed that salt accumulation was concentrated in the root-zone just beneath the plant. This finding can be explained by the heavy texture of soil, which obstructs the leaching operation also by the high root density of tomato under this profile. Soil salinity maps reveal that salt accumulation in the root-zone gets more and more as the growing stage progress. Significance and Impact of the Study: Deficit irrigation treatment reduce the amount of total salt accumulated in the root zone compared with the full irrigation treatment due to the fact that the amount of water applied with deficit irrigation is half of the full treatment, hence salt accumulation. [ABSTRACT FROM AUTHOR]

Modeling heterogeneous reservoirs is cumbersome as it requires a great effort to determine the variation of properties with respect to direction, while the lack of adequate data makes this a difficult task. Generating static models for heterogeneous reservoirs remains an important challenge in petroleum engineering applications which requires more investigations. Some heterogeneous reservoirs, such as thin bed reservoirs, may be divided into some homogeneous subzones where characterization of these homogeneous sub-reservoirs and their integration can represent the properties of the heterogeneous reservoir. To investigate this concept, in this paper, three exemplar reservoirs (ER) were generated. The heterogeneity in the data is increased from ER1 to ER2 and ER3. In the first step, each reservoir was studied as one single zone, so the results can be compared with the proposed method in this work. Ordinary kriging (OK) and multilayer perceptron neural network (MLP) were used for modeling of these exemplar reservoirs. This study showed that OK cannot model reservoir characteristics, whereas the MLP yielded reasonably acceptable results. In the next step, a hybrid clustering classification-based method was applied to divide the reservoir to homogeneous subzones. Each reservoir was modeled in terms of its homogeneous subzones. The homogeneous subzones were modeled using OK and MLP. The results showed that the developed model was successful in modeling the heterogeneity at a reasonable CPU processing time. Also, it was seen that in case of using the simple modeling techniques, MLP neural network yields more reasonable results, compared with OK. [ABSTRACT FROM AUTHOR]

A geostatistical survey for soil requires rational choices regarding the sampling strategy. If the variogram of the property of interest is known then it is possible to optimize the sampling scheme such that an objective function related to the survey error is minimized. However, the variogram is rarely known prior to sampling. Instead it must be approximated by using either a variogram estimated from a reconnaissance survey or a variogram estimated for the same soil property in similar conditions. For this reason, spatial coverage schemes are often preferred, because they rely on the simple dispersion of sampling units as uniformly as possible, and are similar to those produced by minimizing the kriging variance. If extra sampling locations are added close to those in a spatial coverage scheme then the scheme might be broadly similar to one produced by minimizing the total error (i.e. kriging variance plus the prediction error due to uncertainty in the covariance parameters). We consider the relative merits of these different sampling approaches by comparing their mean total error for different specified random functions. Our results showed the considerable benefit of adding close‐pairs to a spatial coverage scheme, and that optimizing with respect to the total error generally gave a small further advantage. When we consider the example of sampling for geostatistical survey of clay content of the soil, an optimized scheme based on the average of previously reported clay variograms was fairly robust compared to the spatial coverage plus close‐pairs scheme. We conclude that the direct optimization of spatial surveys was only rarely worthwhile. For most cases, it is best to apply a spatial coverage scheme with a proportion of additional sampling locations to provide some closely spaced pairs. Furthermore, our results indicated that the number of observations required for an effective geostatistical survey depend on the variogram parameters. Highlights: We compared spatial coverage and spatial coverage plus a subset of 10% from the total sample as close‐pairs.The objective function encompasses variogram uncertainty and prediction error variance.Spatial coverage schemes always performed poorly because of the lack of information at short distances.Using a scheme in which 10% of the sampling units are taken at short distances is a robust strategy. [ABSTRACT FROM AUTHOR]

Geostatistics was applied for the parameters optimization in the estimation process for the Shambesai gold deposit in Kyrghyzstan. Surpac and Supervisor were used as the main geostatistics analysis and block model estimation software. Refereeing to the characteristics of the mineralization type of the Carlin gold deposit, parameter optimization methods were used at different stages of estimation: the sampling spacing, top-cut, proportional effect and mixed distribution were researched for creating robust experimental variogram; logarithmic model was used for model fitting and zonal nesting method was applied to calculate the best fitting variogram fitting curves; parameters of slope of regression, Kriging efficiency and block variance were used for the optimization of block model estimation parameters, and finally the resource was completed with reasonable setting and best accuracy. [ABSTRACT FROM AUTHOR]

Understanding the spatial distribution of organic and/or inorganic contaminants is crucial to facilitate decision-making of rehabilitation strategies in order to ensure the most appropriate management of contaminated sites in terms of contaminant removals efficiencies and operating costs. For these reasons, various interpolation methods [Thiessen Polygon (TP) method, inverse of distance (IDW) method, ordinary kriging (OK), as well as sequential Gaussian simulations (SGS)] were used to better understand the spatial distribution of As, Cr, Cu, pentachlorophenol (PCP) and dioxins and furans (PCDD/F) found onto a specific industrial site. These methods do not only vary in complexity and efficiency but also lead to different results when using values coming from the same characterization campaign. Therefore, it is often necessary to evaluate their relevance by performing a comparative analysis. The results showed that ordinary kriging (OK) was a better estimator of the mean and more advanced compared to the two other methods of interpolation (TP and IDW). However, it appeared that SGS has the same power than OK but it also permitted to calculate a reliable value of the probabilities of exceeding regulatory cut-offs of contamination. Image 1 • Fate and behavior of PCP, PCDD/F, As, Cr and Cu on an treated wood storage site. • Mapping the spatial distribution of (in-)organic contaminants on an industrial site. • Comparison of interpolation and SGS method to assess volume of contaminated soils. • SGS was the most suitable method for risk assessment of contaminated soil. [ABSTRACT FROM AUTHOR]

The continuous and indiscriminate use of NPK fertilizers for boosting productivity in the farming sector longer periods resulted in the imbalance of soil nutrients in the long run. This nutrient disparity in soil gradually throw back in crops, animals and human beings, leading to various degenerative and deficiency related diseases now more than ever. This constrained site specific nutrient management for crops, which essentially rely upon evaluation of variability spatially and temporarily. In this study we scrutinized the spatial variation of soil fertility in exhaustive plough lands of Thrissur district, Kerala, India. A total of 600 geo referred soil samples were collected from different agroecological units of the district and examined for selected micronutrients such as Fe, Cu, Mn, Zn and B. Geo-statistical mapping tool (Arc GIS10.2.2) was used to quantify the degree of spatial variability in various soil fertility parameters. The spatial variation of nutrients in the study area was assessed by using semivariogram method in kriging interpolation and spatial dependence was calculated. The best fit model was applied to the kriging interpolation according to the determination coefficient, which is the correlation of measured and predicted values on space and spatial distribution maps of all the micronutrients. Among the variables analyzed, B revealed strong spatial dependence (24%), Zn with weak spatial dependence (78%) with model gaussian and others with moderate spatial dependence. The results from the present study call to develop a strategy for site-specific management for the parameters showing moderate spatial dependence and weak spatial dependence. But for B, showing strong spatial dependence, only uniform management is needed because it was greatly affected by the structural factors such as climate, topography and parent material. Spatial variability of soil properties is essential for precision agriculture because soil parameters with little or no spatial dependence will not be conducive to site-specific management, and will be managed on the average level only. [ABSTRACT FROM AUTHOR]

After a series of intentional Global Positioning System (GPS) jamming attacks impacted a large area of South Korea, the Ministry of Oceans and Fisheries of South Korea considers long-range navigation (Loran) and enhanced Loran (eLoran) as a maritime backup navigation system. Despite its robustness to signal jamming, the positioning accuracy of Loran/eLoran is lower than that of GPS. Because the signal delay due to the land path, which is called the additional secondary factor (ASF), is the largest unknown component of Loran/eLoran, it is necessary to account for temporal and spatial ASF errors to ensure high accuracy. The generation of ASF maps based on ASF survey data in a service area is the most convenient way to mitigate spatial ASF error, but the quality of ASF maps depends on the applied interpolation algorithm. It is desirable to generate high-quality ASF maps based on ASF measurements at only a few survey points, because extensive ASF surveys are expensive and time consuming and require considerable effort. This paper proposes kriging methods for satisfying this objective and shows their superior performance during a field test in Incheon, Korea. In particular, universal kriging with the proposed drift model showed a better performance than linear interpolation, inverse distance weighing, and ordinary kriging when the test vehicle was close to a coastline. The positioning accuracy with the ASF maps generated by the proposed universal kriging along a 5-km route during the field test was 25.24 m (95%). The land vehicle used for the test experienced significant signal-to-noise ratio (SNR) degradation owing to the noise caused by its engine. A vessel without such SNR degradation is expected to achieve higher accuracy. [ABSTRACT FROM AUTHOR]

The knowledge of spatial variability of soil properties is useful for agricultural management decision making. This study was aimed at examining the spatial variability patterns of soil organic carbon (SOC) in a representative watershed in submontane Punjab, at field, landscape and watershed scales using geostatistical tools. Six land use systems from the Hoshiarpur district of Punjab, India lying in the Satluj Lower Sub basin watershed (code A01SUL11) were selected. Highest SOC content (0.26%) was found in the poplar agroforestry system and the least (0.16%) in the maize land uses system. The brick-kiln system showed a higher variability in SOC content (52%), followed by sesame (32.5%) and mango system (31.3%). Microbial biomass carbon (MBC) and dehydrogenase activity (DHA) did not show a consistently positive relation with SOC. Sensitivity analysis conducted to ascertain sample size for detecting a critical change in SOC content showed that about 5000 samples were required for detecting a 0.01% critical change in SOC in the maize land use compared to fewer samples in other systems. Kriged surface was generated by using ordinary kriging for soil organic carbon in all land use types and all variables (physical, chemical and biological) showed variable degree of spatial dependence. The study revealed the potential effects of management practices on the spatial distribution of measured parameters. Geostatistical simulation showed better performance than kriging in general. Agro-forestry systems especially poplar-based showed considerable promise in carbon sequestration. Several variables especially SOC and texture varied with change in longitude and latitude, thus signifying the need for further examination of spatial variability at watershed scale. [ABSTRACT FROM AUTHOR]

Knowledge about the soil fertility status and mapping their spatial distribution play a crucial role for sustainable planning of particular area. Thus, a study was conducted to assess the soil fertility status of the Agricultural Research Station, Bijayanagar, Jumla, Nepal. The farm is situated at the latitude 29.273656°N and longitude 82.180967°E as well altitude 2370masl. The total 18 samples were collected randomly at a depth of 0-20 cm by using soil sampling auger. A GPS device was used for determination of geographical position of soil sampling points. The collected samples were analyzed following standard analytical methods in the laboratory of Soil Science Division, Khumaltar. The Arc-GIS 10.1 software was used for the soil fertility distribution mapping. The observed data revealed the structure was sub-angular blocky, whereas colour were dark grayish brown and very dark brown. The sand, silt and clay content were ranged 27-47%, 33.10-61.10% and 11.90-23.90%, respectively and categorized loam and silt loam in texture. The soil pH was moderately acidic to moderately alkaline (5.45-7.66) and very low in available boron (0.01-0.28 mg/kg) and sulphur (0.59-6.23 mg/kg). Moreover, very low to very high available iron (15.90-300.50 mg/kg), very low to high available manganese (1.46-12.88) and low to high organic matter (2.07-6.53%). Similarly, medium to high total nitrogen (0.14-0.23%), available potassium (40-255 mg/kg) and zinc (1.12-8.26 mg/kg). Correspondingly, high available calcium (1632-2880 mg/kg) and magnesium (98-456 mg/kg), and very high available phosphorus (64.2-257.2 mg/kg) and copper (2.58-12.16 mg/kg). The determined soil test data can be used for sustainable soil management as well as developing future research strategy in the farm. [ABSTRACT FROM AUTHOR]

• Development of a new methodology of spatiotemporal rainfall monitoring network. • Optimal rain gauge network based on spatial and spatiotemporal precipitation. • Combination of multi attribute decision making and heuristic optimizations. • Heuristic approach is coupled with OK and BME. Precipitation data plays an important role in investigation of water-related fields of research such as water resources management, hydraulic structure design and groundwater quantity/quality parameters due to its high variability in space and time. To evaluate and investigate precipitation pattern, a set of well-designed rain gauge stations can substantially reduce the cost and increase the estimation accuracy. In the present research, a new methodology of spatiotemporal optimization is developed for a rain gauge monitoring network and the results are compared to the optimized network based on spatial variations of precipitation. The optimization process consists of two main steps of application of multi attribute decision making and heuristic approaches. The entropy is chosen as the multi attribute decision making approach to determine optimum number of stations. Then, the optimization process is implemented via coupling of Genetic Algorithm (GA) and geostatistical methods to identify the best rain gauge network configuration. To determine the spatiotemporal structure of precipitation, spatiotemporal variography and geostatistical methods known as Ordinary Kriging (OK) and Bayesian Maximum Entropy (BME) have been undertaken. Thirty years of annual precipitation data from 105 rain gauge stations within and near Namak Lake watershed in the central part of Iran are utilized in this research to optimize rain gauge stations spatially and temporally. Results showed that spatiotemporal network design considerably differs from spatial optimal rain gauge stations. Optimal locations of rain gauge stations resulted from spatiotemporal approach are almost two times better than spatial configuration to reduce rain gauge costs (installation, operation, maintenance, etc.) by avoiding data redundancy more efficiently. In addition, BME-based network design method outperformed OK-based network. [ABSTRACT FROM AUTHOR]

Abstract Masson pine (Pinus massoniana) is a typical reforestation species in subtropical China, which plays a key role in soil and water conservation. Site-specific forest management requires an accurate estimation of the soil organic carbon (SOC) stocks, and information about the spatial distribution of SOC stocks is essential for improving the soil quality and ecosystem productivity. We examined the spatial distribution of SOC stocks using 91 soil samples from Masson pine forests in subtropical China. Ordinary kriging (OK) and inverse distance weighting (IDW) methods were used to compare the spatial patterns of the SOC stocks. A moderate spatial dependence of the SOC stocks suggested that extrinsic and intrinsic factors affected the SOC stocks. Similar spatial distributions but different cross-validation accuracies indicated that OK outperformed IDW. The soil pools at a depth of 0–60 cm were 774.06 Gg and 761.61 Gg as determined by the OK and IDW methods, respectively, which were higher than that measured by the conventional method (CM, 734.22 Gg). This highlights the need to apply different methods when studying the regional SOC pools. On the basis of the comparison of OK, IDW, and CM methods, OK is recommended for determining nonhomogeneous sampling point distributions. The present results enhance our understanding of method selection when studying the spatial distribution of SOC stocks. Highlights • Distribution of SOC was determined in Masson pine forests in subtropical China. • Ordinary kriging, inverse distance weighting, and conventional methods were used. • Ordinary kriging was better for determining spatial distributions of SOC. [ABSTRACT FROM AUTHOR]

Soil texture is a key controlling factor of soil properties and its functions include water and nutrient holding capacity, retention of pollutants, root development, soil biodiversity, and biogeochemical cycling. From the geotechnical standpoint, it is interesting to analyze the soil texture in regions due to its relation with the infiltration and runoff processes and, consequently, the effect on erosion processes. The purpose of this study is to present a methodology that provides the soil texture spatial variation by using Fuzzy logic theory and geostatistical technique in Geographic Information System (GIS) platform. A total of 140 soil samples were taken from topsoil (0–30 cm) in the study area located in the north of Guilan Province, the southern coast of Caspian Sea, Northern Iran. The soil textural classes were converted to numerical values (fuzzy values) using the fuzzy logic concept. The fuzzy values were spatially interpolated by ordinary kriging method such that the fitted model on experimental semi-variogram was exponential with moderate structure. The results showed the accuracy of soil texture predictive map was acceptable according to the values of normalized root-mean-square error for train data set (0.182) and test data set (0.179). The knowledge of the spatial variability of soil properties such as the soil texture can be an important tool for land-use planning in order to reduce the potential soil losses during rainy seasons. The results indicated that the integration of fuzzy logic, geostatistics, and GIS can improve the interpolation process. [ABSTRACT FROM AUTHOR]

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In this study, the Water Quality Index (WQI) was calculated for drinking water samples collected from an area of Mardan district. A total of 30 water samples were collected from water distribution systems at the end user end. The samples were analyzed for 21 parameters namely, C, pH, EC, Do, TDS, NTU, TH, Ca, Mg, Na, K, SO4, NO3, Mn, Cu, Fe, Zn, Pb, Ni, Cd and Cr. Standard methods were used for the analysis of the physicochemical parameters while heavy and trace metals analyzed using Atomic Absorption Spectrometer. For the calculation of Water Quality Index, all parameters were subjected to screening using principal component analysis, thus reducing the original number of parameters to a final list of ten. The final list of parameters was used to calculate and map WQI. Moreover, two methods of kriging Ordinary Kriging and Empirical Beysian Kriging were compared for their performance in modelling the spatial distribution of the selected parameters in the study area. It was found that EBK performance was more appropriate as compared to other spatial variability models for the majority of the variables. [ABSTRACT FROM AUTHOR]

Ore/waste classification and economic evaluations of mineral deposits rely on the grade of elements of interest, which must be predicted as accurately as possible to minimise misclassifications. Ordinary kriging is commonly used for such a purpose, but non-linear predictors such as disjunctive kriging may improve the results. In this context, this work presents two case studies, in one of which (gold grades with heavy-tailed distribution) disjunctive kriging outperforms ordinary kriging, while in the other case study (copper grades with a moderately skewed distribution), it turns out to be as accurate as ordinary kriging, although with less conditional bias. [ABSTRACT FROM AUTHOR]

Problem definition: Spatial information on salinity is required at the farm level to enable suitable soil, crop and water management practices. Rationale: To facilitate this, we used an electromagnetic (EM) induction instrument for rapid measurement of apparent soil electrical conductivity (ECa—mS m–1) across the 11 ha area of the Central Soil Salinity Research Institute experimental farm in Nain, Haryana, India. Methods: The ECa was measured using an EM38 in horizontal (ECah) and vertical (ECav) modes on a grid survey. Using the ECa data, we selected 21 locations using the response surface sampling design (RSSD) module of Electrical Conductivity Sampling Assessment and Prediction (ESAP) software. We collected soil samples at four depth increments, including two topsoil (0–0.15 and 0.15–0.30 m), a subsurface (0.3–0.6m) and a subsoil (0.6–0.9m) and measured the soil electrical conductivity (ECe—dS m–1). Results: We developed multiple linear regression to predict ECe using the ESAP software from ECah and ECav and two trend surface parameters (i.e., Easting and Northing) across the farm. The prediction accuracy and bias were compared at different depth increments, and results of the spatial distributions of ECe using ordinary kriging (OK) interpolation were described in terms of the crop and soil use and management implications. Conclusions: We conclude the overall approach allows for generations of a digital soil maps (DSMs) of ECe which serve as baseline data that will allow the monitoring of any rehabilitation effort of salt‐affected soils according to their actual degree of salinity. [ABSTRACT FROM AUTHOR]

Background: A great number of studies have shown that the distribution of microorganisms in the soil is not random, but that their abundance changes along environmental gradients (spatial patterns). The present study examined the spatial variability of the physicochemical characteristics of an extreme alkaline saline soil and how they controlled the archaeal and bacterial communities so as to determine the main spatial community drivers. Methods: The archaeal and bacterial community structure, and soil characteristics were determined at 13 points along a 211 m transect in the former lake Texcoco. Geostatistical techniques were used to describe spatial patterns of the microbial community and soil characteristics and determine soil properties that defined the prokaryotic community structure. Results: A high variability in electrolytic conductivity (EC) and water content (WC) was found. Euryarchaeota dominated Archaea, except when the EC was low. Proteobacteria, Bacteroidetes and Actinobacteria were the dominant bacterial phyla independent of large variations in certain soil characteristics. Multivariate analysis showed that soil WC affected the archaeal community structure and a geostatistical analysis found that variation in the relative abundance of Euryarchaeota was controlled by EC. The bacterial alpha diversity was less controlled by soil characteristics at the scale of this study than the archaeal alpha diversity. Discussion: Results indicated that WC and EC played a major role in driving the microbial communities distribution and scale and sampling strategies were important to define spatial patterns. [ABSTRACT FROM AUTHOR]