جستجوی مقالات مرتبط با کلیدواژه « variogram » در نشریات گروه « جغرافیا »

Spatial changes in soil properties are significantly influenced by factors affecting soil formation such as topography. Topography can create different properties in the soil by affecting the spatial distribution of effective environmental parameters. Topographic features such as contour, slope direction by affecting soil temperature, evaporation capacity, soil moisture, soil organic matter, precipitation, movement and accumulation of soil solution can affect the biological properties of soil. Respiration of soil microorganisms is one of the most sensitive and important biological properties that can affect soil quality. To study the impact of land use change on soil ecosystem performance due to human activities, it is necessary to maintain and restore soil capacity to provide ecosystem services. The aim of this study was to investigate the effect of slope and height classes on soil microbial respiration in agricultural, rangeland and forest areas of Khanghah Namin village.

In order to conduct this research, in the fall, samples of intact and untouched soil from the study area were prepared in 72 points by regular networking method with distances of about 200 meters. Common physical and chemical properties of soil including soil pH, electrical conductivity, equivalent total calcium carbonate, organic matter, soil texture (percentage of sand, silt and clay particles) and total porosity were measured. From soil biomarkers, basal microbial respiration and substrate-stimulated respiration were determined. Kriging geo statistical method was used to estimate and determine the spatial distribution and evaluate some soil properties as well as cumulative soil quality indicators. For this purpose, the geographical coordinates of each point of the GPS device were transferred to GIS software. After recording the data of the sampled points, the interpolation command was executed and the information of the non-sampled points was obtained. In order to explain the spatial similarity of the variables, the experimental variogram of the data was obtained using GS + statistical software and the best variogram model was selected according to the variogram information. Finally, the distribution map of soil biological indicators was extracted using GIS and Kriging geo statistical methods. The correlation between the measured and estimated values of soil biological properties was calculated and evaluated in SPSS19 statistical software. Experimental factors included three land uses (namely, agriculture, rangeland and forest), slope direction at two levels (north and south) and two elevations (<1580 <) in terms of significant mean difference in field moisture. Normalization of data was performed by Shapiro-Wilk (1965) test using SPSS19 software. Mean comparison was performed unpaired by T-Test using SPSS19 statistical software. Pearson correlation between soil microbial base respiration and substrate-stimulated respiration with other soil properties was obtained in SPSS19 software.

The results of the descriptive statistics of the soil properties showed that most of the soils in the study area have a loam soil texture class. The pH value of the samples was in the range of 5.7.7, the percentage of organic carbon was 0.6.6 % and calcium carbonate value was from 0.67 % to 16.67 %. The maximum electrical conductivity (EC) was less than 2 ds/m of Siemens per meter, indicating the non-saline soils of the area. Also, the high levels of average microbial base respiratory and the substrate induced respiration were 1.21 and 7.5, respectively. High correlation between the properties of soil organic carbon percentage, porosity percentage, silt percentage, farm moisture percentage and soil pH with base microbial respiration and substrate induced respiration with clay, sand and electrical conductivity of the soil were not significant. The farm moisture had the highest hydraulic soil with the lowest correlation with microbial respiration. In general, the average base and substrate induced respiration was higher on the height contour (1580<). Also, both base and substrate induced respiration in accordance with elevation had a significant mean difference at 5 %. In the present study, the studied slope on the north and southern slopes on the base microbial respiration level at 5 % probability level and substrate induced respiration with the probability level of 1 % was significantly different. The highest amount of these indicators was measured on the northern slope of the area and the lowest in the south slope of the southern south slope located mostly in the center to the northeast of the area and the northern slopes in the southern and northern parts of the area. It is maintained that the cause can be attributed to the existence of heights and solar radiation on the southern slopes. To determine the distribution of soil biological indexes at the level of the study area, the distribution of the area's biological properties of the area was prepared by applying the Kriging method. The best model for drawing soil base respiration map was selected for substrate induced respiration with the largest soil bed based on the highest R2, the range of impact, the least RSS and the effect of the piece. The largest amount of soil respiration was observed by the base respiration and irritated respiration in the north and northeast of the area. It is also observed that as the height of the area increases from west to east, basic respiration is generally stimulated with a relatively increased trend. The results show that there is a significant correction in both characteristics of measured and estimated values ​​by the Kriging internalization method.

In this study, the amount of basal and bed-stimulated respiration in forest lands was significantly higher than rangeland and agricultural lands. Between rangeland and agricultural uses, the amount of soil microbial respiration in agricultural lands was lower. Due to the fact that this area is a tourist destination and there are local livestock, this finding can be attributed to transportation, high human and livestock activity at lower altitudes, and agricultural and pasture use, which are more exposed to these activities. The microbial respiration values ​​in the northern direction of the slope were significantly higher than in the southern direction. The southern slopes received more solar energy than the northern slopes. Usually in the south-facing slopes, the number and activity of soil microbial community is low due to high temperature, low water, low porosity and adverse consequences due to inadequate vegetation. In addition, a significant mean difference was observed in the northern and southern directions of the slope. Also, the increase in altitude due to the relative increase in humidity and decrease in temperature led to an increase in microbial respiration. This issue can significantly affect the activity and respiration of soil microorganisms by affecting soil processes and its development as well as the amount of vegetation in the two slopes. According to the obtained results, it can be stated that the evaluation of biological properties that was discussed can play a prominent role in controlling the management factors in the area. Large changes in soil respiration indices are probably due to the high sensitivity of these properties to human factors such as tillage operations and the use of chemical fertilizers, which cause changes in soil surface uniformity. The zoning map of these two features also confirmed this. The results showed a logical correlation between soil microbial respiration and other characteristics that the existence of a correlation between soil properties indicates a strong relationship between them. Geo-statistical science in this study was able to estimate the properties well. The results of this study showed that in the studied area, biological indicators depend on the position of the landscape and land use, and these factors can affect the structure, development and evolution of soil by affecting the structure of the microbial community.

Heavy metals are known to be the most dangerous environmental pollutants because they do not decompose by physical processes and therefore remain for a long time and will be effective in biochemical cycles and ultimately in the human food chain, disrupting biological reactions and damaging organs. And they will even die. Therefore, in order to control the quality of the environment, it is important to study the heavy elements in the soil. So far, much research has been done to identify contaminated areas and implement sustainable land use policies with respect to the risk of contamination. In this study, the spatial distribution of nickel in surface soils (0-10 cm) in parts of Qom province with urban-industrial, agricultural and uncultivated land uses with an area of 883 Km2 was investigated. In this study, geostatistical tools (ordinary kriging) were used. Sampling was performed at 209 points on a grid with distances of about 1.5 × 1.5 km in agricultural and urban land use and about 2×2 km in uncultivated land use. Concentration of total nickel and absorbable soil was measured by atomic absorption spectrometer. Statistical and geostatistical studies were performed by SPSS, Variowin and WINGSLIB software and contamination map was drawn with Surfer16 software. The results showed that the concentration of nickel in any of the forms in the region did not show toxicity. But there were significant differences in land uses. Existing industries, parent materials, and nickel-rich basic and ultra-basic rocks, the use of phosphate fertilizers and sewage sludge may have increased nickel, and the direction of westerly winds has led to the transfer of nickel in the region.

Understanding the status of discharge and sediment of watersheds area provides an understanding of the hydrological processes. Uniform assuming of discharge and sediment in a watershed leads to the application of unit planning and management for the entire watershed and leads to a failure to achieve the goals. The present study was conducted with the aim of studying spatial variations and zoning of discharge and sediment values in Ardabil province during 10 years (2005-2014) at 37 stations with hydrometry in Ardabil province. IDW interpolation methods, simple kriging, ordinary kriging and cokrigring were investigated to determine the best method for studying spatial variaions, discharge and sediment variables. Based on the results of the evaluation indices of IDW (RMSE=1.172, MAE=0.644, MBE=-0.2, MSE=1.373) and cokriging (RMSE=0.107, MAE=0.08, MBE=-0.001, MSE=0.011) and the correlation coefficient between measured and estimated values by IDW (r=0.429) and cokriging (r=0.554) respectively, as the best method for evaluating the discharge and sediment variables were chosen. Based on the results of the IDW method, the amount of digestion in the Western parts of Ardabil province is greater (4.84-5.8 m3/s), and in the central section, this amount is the lowest (0.04-1 m3/s). According to the results of the interpolation of the sediment variable with the cokrigring method in the Western and Southern parts of Ardabil province with a higher amount of sediment (0.16-0.19 ton/ha/year) and in the central parts of the sediment deposition (0.01-0.04 ton/ha/year).

Much of Iran is dry and poor water, and the water required by villages, industries and cities is used by groundwater sources. Excessive groundwater harvesting in many parts of the world has caused a severe drop in groundwater levels. Therefore, this study was conducted to investigate the spatial and temporal variations of groundwater level in Khorramabad plain over a ten year statistical period (2004-2014) using the best method, geostatistical estimator. In this regard, firstly, the available groundwater statistical data were collected and various conventional and simple kriging and IDW (RBF) methods with Gaussian, exponential and circular models were used. The mediation method was performed using two criteria (ME, RMSE). Then their location maps were plotted in Arcmap 10.3. The results showed that the multi-quadratic photo-distance method (RBF) is the best groundwater interpolation method in 2004 and 2014.

Rainfall erosivity is defined as the aggregative power of the rain. If other effective features on soil erosion be considered constant then soil loss could be directly connected to rainfall erosivity. Rain erosion term was proposed by Wichmeier and Smith in 1978 to consider the effect of climate on raw erosion. Measurements of meteorological parameters by the traditional methods require a dense rain gauge network. But, due to the topography and cost problems, it is not possible to create such a network in practice. Given the significant change in rainfall in time and space on the one hand and low rain-gauge stations to record rainfall on the other hand, the necessity of using geostatistical methods for rainfall erosivity mapping is inevitable. Geostatistical methods use the spatial correlation between observations in the estimation processes. In these cases the spatial distribution pattern of rainfall erosivity can be produced using different methods of interpolation. Study area Lorestan province is located in southwest of Iran and covers an area of 28249 square kilometers. It is located between the latitudes 32º 37' and 34º 22' N and the longitudes 46˚51ʹ and 50˚30ʹE. The main objective of this research were: (1) analyze the spatial distribution of rainfall erosivity using two different interpolation methods namely ordinary Kriging and simple Kriging; (2) put forward the best interpolation method through cross-validation, construct the high resolution grid data of rainfall erosivity and provide the reliable information for relevant researches
 In the first step, the precipitation data collected from 53 precipitation stations and Modified Fournier Index (MF) calculated based on Eq. (1)
                                                                                                (1)
 Where MF is the modified Fournier index value (mm), pi  is averagemonthly precipitation (mm) and P is average annual precipitation (mm). Then Eq. (2) and Eq. (3) were used to estimate rainfall erosivity or R-factor values (MJ mm ha -1 h -1 year -1).
 
It is suggested that Eq. (2) be applied for locations with MF values less than 55 mm and Eq. (3) be used for locations with MF values greater than 55 mm.
  In this article two interpolation techniques namely simple and ordinary Kriging were compared in GS+5.1.1 and ArcGIS10.3 software’s in order to determine which one describe better the spatial distribution of rainfall erosivity. Kriging methods assume that the spatial variation of a continuous climatic variable is too irregular to be modeled by a continuous mathematical function, and its spatial variation could be better predicted by a probabilistic surface. The predictions of Kriging-based methods are currently a weighted average of the data available at neighboring sampling points (weather stations). The weighting is chosen so that the calculation is not biased and the variance is minimal. A function that relates the spatial variance of the variable is determined using a semi-variogram model which indicates the semi variance between the climatic values at different spatial distances. The resulting maps from interpolation were compared by using a set of validation statistics include Mean error (ME), Mean Standardized Error (MSE) and the root mean square error (RMSE) by Eq. (4), (5) and (6)
  Based on the results, rainfall erosivity values varied from 11.1 to 749.5 MJmmha−1 h−1 y−1. Differences between the simple and ordinary Kriging models regarding the validation statistics were narrow, but allowed for a comparison. The obtained results showed that ordinary Kriging with higher R2 and lower ME, MSE and RMSE had better precision in mapping rainfall erosivity. The spatial distribution of rainfall erosivity showed the areas along north-south of Lorestan province and central regions had higher values while lower rainfall erosivity was seen in the western and eastern areas of the study area. The availability of high-quality environmental maps is a key issue for agricultural and hydrological management in many regions of the world.  Produced rainfall erosivity map in this research can be used for estimation of soil loss by USLE model. Rainfall erosivity maps also can be suitable as guidance for soil conservation practices and identifying areas with the high potential of soil retention as an ecosystem service. Further research may be directed to find reliable erosivity indices which can be computed from daily precipitation data.

Salinity is one of the most destructive processes in soils, particularly in arid and semi-arid areas. In order to use and exploitation of such soils, the soil monitoring and mapping are necessary. In this study, in order to zoning and mapping of soils, sampling was collected based on network method from Bolagh (Saveh) saline lands and the electrical conductivity of saturated soil extract was determined. Then the data had been transferred to the ArcGIS 10 software and soil mapping had been drawn. The exponential model of Semivariogram showed the best cross-validation and efficiency than the other models (spherical, linear and gussian). Also, the amount of nugget effect to the threshold was 84/43% which indicates that the medium spatial correlation for soil EC amounts in the study area. Also, the variogram effect range was calculated about 261 meters. Assessment of the resultant index indicates that the geostatistical has been able to soil salinity mapping with moderate accuracy and precision. The results showed that among five soil salinity classes, the soils of study area classifying in four Classes (including non-salinity, low salinity, medium salinity and high salinity) which highest quantities are related to middle southern regions. These results show that soil salinity in this area has high variability.

Introduction One form of precipitation is snow. Due to the long-lasting process of its transformation into runoff, it is different from other ingredients of the water budget. In most permanent rivers whose basins are covered with snow, it plays a little role in water resource's studies. This case study is the Gush Bala mountain watershed, which is located in the eastern part of Mashhad in Khorasan-Razavi province.
Materials and methods In this study, 11 measured samples were used to map the depth and density of snow. Using Minitab software, the normality of the gathered data of snow was assessed through Kolmogorov-Smirnov test. The probability of more than 0.05 was considered as a criteria for the normality of the distribution of the data. If it does not have a normal distribution, they are normalized, through using modified shapes, in regard to their skewness. After testing the normality, the point data was transformed to regional data through Geo-Statistics such as Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Kriging, and Cokriging. Geo-statistical estimation consists of two phases. Its first phase involves identifying and modeling spatial structure that can be studied by means of half-changing facade and estimation which can be the best linear unbiased estimation. The mutual assessment method was utilized to choose the most appropriate Geo-Statistical method. In this method, for each step, one observed point was crossed out and its value was estimated. After that, the estimated value was compared with the observed one.
Results and discussionThe most vital criterion was Root Mean Square Error (RMSE).The comparison of the RMSE of different methods like IDW, RBF, Kriging, and Cokriging showed that the most and the least values were for simple cokriging and simple kiriging methods whose values were respectively 0.518 and 0.023. Therefore, the simple kriging revealed better results than the other methods. Overall, less values of RMSE led to a better performance of a spatial semi-variogram for depth and density. Because the values of RMSE for 11 functions including Circular, Spherical, Tetra spherical, Penta spherical, Exponential, Gaussian, Quadratic Rational, Hole Effect, K-Bessel, J-Bessel, and Stable for simple Cokriging for depth and simple kriging for density were respectively 0.518 and 0.023, the interpretation Variogram for 11 functions was needed in the case of simple cokriging for depth and simple kriging for density.
The criteria which were used were Nugget and Nugget to sill ratio. Generraly, if they are less, their results are better super structure. The value with simple cokriging method for the depth of the snow for J-Bessel and sill ratio were respectively 0.795 and 0.95 which experienced better super structure. The value with simple kriging method for the density of the snow for J-Bessel and sill ratio were respectively 0.806 and 0.9 which showed an optimum method compared to other methods. All values that are obtained from interpolating kriging and cokriging methods must be evaluated with variogram structure, especially Nugget and Nugget to sill ratio. If the values of Nugget and Nugget to sill ratio increases, the predictability of the variogram decreases. In the variogram which was related to the depth and density data, the piece effect showed high levels. Furthermore, the ratio of nugget to sill was more than 0.75, which revealed a weak super structure between values in various distances. These findings demonstrated the heterogeneity of the data. On the other hand, considerable oscillations between nugget in depth data and density was shown on high values for nugget.
Conclusion The results of the predictions assessment done with simple cokriging for depth and simple kriging for density showed the higher accuracy of the aforementioned methods to other methods. One reason for this high accuracy can be ascribed to the influence of these parameters. In general, when the environment is more homogeneous, the Mardr small scale's results will be better than conventional statistical analysis. Thus, it seems that the sampling method with homogeneous units using satellite and aerial images could result in the homogeneity of the data. In addition, as a result, the spatial vacillations of the data went down and the ability of Geo-Statistics to predict and estimate will improve.

human beings from the beginning of creation until now always with a variety of injuries, wars and invasions and faced disaster have been. And damage to life and property is much to them Entered The targeting of cities, especially and hospitals,to demoralized and inflict economic damage and disruption of social order is Since Ahvaz during the eight years of war Is attacked has been. The aim of this study is That to Location the hospital of Ahvaz with an emphasis on passive defense
Type Research Development of applied research methods Check is Analytical . Our study area eight is Area in Ahwaz. Data analysis has been conducted With is the theoretical foundations And using geographic information system And Fuzzy Share And the use of variogram model
The findings from the study show. That At The planning and location for hospitals in Ahvaz. Overlay maps resultant the model The factor and fuzzy gamma 1.6 = γ, were appropriate. At situation present, Between the 22 hospitals in city Ahvaz Only 4 hospitals in the number 4 region From the perspective passive defense In a good condition.
The results show that in the city Ahvaz With regard to economic status That In the country As well as geopolitical position and historical background of In terms of the threat of war At Past and present a very important city in Iran.in addition. Hospitals in the city are not suitable for passive defense And is require a more detailed plan and structured strategies.

Analysis of the spatial pattern is an important element for the optimum management and having knowledge about ecosystem function. This study was conducted to analyze the spatial pattern of trees and shrub in the riparian forest landscape of Karkhe, also for assessment of their spatial variability regarding the distance from the river. The number of trees and shrubs were sampled using 117 plot (20m × 20 m) along parallel transects (perpendicular to the river). The distance between transects was 500 m. The variograms of Populus euphratica and Lycium shawii were spherical. Tamarix sp. and total of them were exponential. All revealed the presence of spatial autocorrelation. The range of influence was 186 m for Tamarix sp., 362 m for Populus euphratica, 749 m for Lycium shawii and 208 for total. The kriging maps showed spatial variability of them. The spatial pattern of species total and Tamarix sp. are similar that was shown in correlation. So Tamarix sp. is a major part of this forest. Populus euphratica is replacing with Lycium shawii. Also the higher distribution is nearby river for all trees and shrub.

Water vapor as one of the most important climate elements, plays an important role in decision-making, design and evaluation in hydrological models. Therefore, understanding the spatial changes of this important climate element have a significant effect on water management and planning. Accordingly, this study is tried to studythe spatial structureand estimation of watervapor pressurein south and south west of Iran, using by geostatisticsmethodand variography analysis. In this regard, the water vapor pressure data of 78 synoptic stations on 18 August 2007 as one of the sweeping days by the water vapor pressure was analyzed. The first step for this purpose was calculation of the spatial variable of water vapor pressure that were analyzed by plotting the variogram. After fulfilling this requirement, geostatistical methods such as simple kriging, ordinary kriging, Co-kriging with auxiliary variables, and circular, spherical, exponential, Gaussian and quadratic rational models were used and their performance were evaluated. The results of cross validation showed that the best method that being able to justify the amount of water vapor pressure is Co-kriging method with altitude. According to the drawn map with the optimum method it was found that near the Persian Gulf and Oman Sea and the North and North West of study area are the highest and lowest amounts of water vapor pressure, respectively. It was determined that three reasons 1. Far and near to the main sources of humidity, 2. Zagros Mountains and 3. The establishment of strong pressure has an important role in water vapor pressure distribution.

Crown dieback is a manifest phenomenon that occurred in the Zagros oak forests during recent droughts. Since the effect of crown features on the presence of mistletoe and on the trees have been proven, it is necessary to investigate the relationship between the dieback and the characteristics of tree canopy of the trees with mistletoe. So the investigation of the spatial distribution of parameters related to dieback of trees is important in order to manage and control the mistletoe in The Zagros Forests and prevent damage to trees and. Therefore, the plots (1600 m2) in a 200 m × 200 m sampling grid were established in Gahvareh Forests. Then Data were analyzed using geo-statistics (variogram) in order to describe and quantify the spatial continuity of variables. The range of the variogram for tree height is: 4110, tree height with mistletoe: 4227, average diameter of the tree crown: 4110, canopy diameter of tree with mistletoe: 4110, crown shape ratio of the tree with mistletoe: 5110, crown area of the tree with mistletoe is 2053 and the percentage of crown dieback of trees with mistletoe is 4110. The range of height, tree height with mistletoe, average diameter of the crown tree, tree crown diameter with mistletoe and the percentage of crown dieback of trees with mistletoe are similar proving that these factors have a greater impact on the distribution of this variable.

Today, soil erosion is considered as one of main subjects of watershed management at national and global levels. Assessment of decreasing risk of soil and it spatial distribution are one of key factors for successful evaluation of soil erosion. The objectives of research were calculation and modeling of rain erosivity factor in Ilam province using data of number of 28 pluviometry stations during of 23 years period. After that, rain erosivity factor was calculated, map of rain erosivity factor was provided using geostatistical algorithms. Spatial correlation between data of rain erosivity was drawn using Variogram. In order to, studying of erosivity index were used (IDW), (GPI), (RBF), (LPI) and kriging. The best algorithm was selected using statistical indices of Root Mean Square Error (RMSE) and MAE. Results showed that, GPI was the best algorithm with RMSE and MAE equal to 28.93 and 20.95, respectively.

Selection of an optimal interpolation method for estimating the characteristics of the not-sampled points was the main aim of this study, due to the important role of data management. In this study, ordinary Kiriging interpolation models including linear, exponential, spherical, Gaussian were used to estimate the mean annual rainfall of Ilam Province. For this purpose the normality of the data was checked using the Kolmogorov-Smirnov method and then the variogram of each model was calculated and plotted. In continuation, the best spatially fitted variogram between the data was used being compared to the other variograms. For this purpose, the relation between the piece effect and the roof of variogram was used (Co+C). According to the parameters obtained from the fitted variograms, the Gaussian variogram with the 0.33 best fitted the correlation between the data and was used for interpolation. In order to evaluate the efficiency of employed models, the root mean square error (RMSE) and the standard error of results were used. The results showed that the Gaussian Variogram having the lowest estimation error (6.12) and root mean square error (166) were the best model for the interpolation of the data in this investigation. Furthermore, comparison of RMSE with Standard Error (SE) for calculating the amount of expectations demonstrated that the four models gave overestimations.

Salinity is a highly important problem in arid and semi- arid region. In Iran، about 235،000 km 2 (or 14. 2% of the total area of the country) area is salt-affected، which is equivalent to about 50% of irrigated lands in Iran (Pazira 1999). Irrigating of these lands causes to transfer salts to the area of root growth and thus increases the osmotic pressure and reduces the absorption of the nutrient elements and product. Some of researchers believe that this process is one of the disasters that threats development of human societies. Esfahan province is exposed to the danger of salinity. Esfahan province is located in the central arid region of Iran. Of 105،000 km 2 total area، an area of 5000 km 2 is used for crop and fruit production. Soil and water salinity is the major limitation to achieve optimum crop yields. However، the classical soil survey methods of field sampling، laboratory analysis and interpolation of these field data for mapping، especially in large areas، are relatively expensive and time-consuming but to get informed about distribution of salinity in soil is very important for recognizing critical threshold، planning، management، operation of source، and suitable distribution of water to correct the soil saline. Soil chemical properties commonly have spatial dependence at regional scale (Yost et al.، 1982). Regional assessment of soil properties requires evaluation of their spatial distribution. In recent years، environmental scientists have come to appreciate the merits of geostatistics and kriging for investigating and mapping Soil chemical properties in un-sampled areas. There are a large number of reports to natural resource distributions. Geostatistic method such as Ordinary Kriging is a class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. Many geostatistical tools were originally developed as a practical means to describe spatial patterns and interpolate values for locations where samples were not taken. Three functions are used in geostatistics for describing the spatial or the temporal correlation of observations: these are the correlogram، the covariance and the semivariogram (hasani pak، 1999). The last is also more simply called variogram. The following parameters are often used to describe variograms: nugget: The height of the jump of the semivariogram at the discontinuity at the origin، sill: Limit of the variogram tending to infinity lag distances and range: The distance in which the difference of the variogram from the sill becomes negligible. In applied geostatistics the empirical variograms are often approximated by model function ensuring validity (Chiles and Delfiner 1999). Some important models are (Chiles and Delfiner، 1999; Cressie، 1993): The exponential variogram model، the spherical variogram model and The Gaussian variogram model. In assessments of some variables، spatial correlation structure is the same in all directions، or isotropic. In this case the variogram depend only on the magnitude of the lag vector، h = h، and not the direction، and the empirical variogram can be computed by pooling data pairs separated by the appropriate distances، regardless of direction. Such a variogram is described as omnidirectional. In many cases، however، a property shows different autocorrelation structures in different directions، and an anisotropic variogram model should be developed to reflect these differences. The most commonly employed model for anisotropy is geometric anisotropy، with the variogram reaching the same sill in all directions، but at different ranges. However some Geostatistical methods such as kriging need valid variograms. Cross validation is used to find the best model among the competitors. “Cross Validation” allows us to compare estimated and true values using the information available in our sample data set (Houlding، 2000). Study area: This research was conducted in Isfahan province (Fig1). It is located in the center of Iran in a predominantly arid or semiarid climate condition and is about 6800 km2 around Zayandehroud River. Mean annual precipitation and temperature are 120 mm and 14. 5 Co and Annual evapotranspiration is 1500 mm. The soils are classified as Aridisols. The area covers different land uses including agricultural، industrial، urban and uncultivated lands. In this study، soil sampling strategy was random stratify. In this method، the region was stratified in to regular- sized grid cells of 4 × 4 km and within each cell a sampling location was chosen randomly. A total of 255 soil samples (0-20 cm) were collected (Fig 2). At each sampling point the coordinates were obtained using a portable GPS and its land use was recorded. After calculation، 46. 5% of the sampling locations occurred in agricultural lands، 43. 5% in uncultivated lands and 10% in industrial and urban area (Fig2). Soil samples were air dried and ground to pass through a 2 mm sieve، Electrical conductivity was measured in a 1:2. 5 soil-water ratio suspension. Statistics including mean، variance، maximum، minimum، coefficient of variation (CV) and comparison average EC in different land use were calculated. The results of factor analysis were used to calculate the autocorrelation value between observed points and produce a minimum unbiased variance estimate. This variance is calculated as a function of variogram model. Variogram is calculated using the relative location of the samples (Soderstrom، 1998). The experimental variogram is calculated for several lag distances this is then generally fitted to a theoretical model and the parameters in suitable model are then used in the kriging procedure (Mohamadi، 2007). The next step is cross validation of the prediction models. The cross-validation technique is used to choose the best variogram model among candidate models and to select the search radius and lag distance that minimizes the kriging variance. Cross-validation is achieved by eliminating information، generally one observation at a time، estimating the value at that location with the remaining data and then computing the difference between the actual and estimated value for each data location. To compare different interpolation techniques، we examined the difference between the known data and the predicted data using the Mean Square Reduced Error (MSE). Correlation coefficient (Pearson) computed between real and estimated data with ordinary kriging. Distribution map of EC was produced using the ordinary kriging and use from maximum 16 point and minimum 3 point in estimation. The descriptive statistical parameters were calculated with Microsoft EXELE and SPSS (version 11). Maps were produce with Surfer (version 8) and ILWIS (version 3. 0) and geostatistics analyses were carried out with VARIOWIN and WINGSLIB. The average EC was 6. 9 dSm-1 with range of 1-74 dSm-1 in Isfahan surface soils (Table 1). EC values don’t follow a normal distribution and had a strongly skewed distribution (Fig 4a) therefore these values were transformed to logarithm and the log-transformed data fit an approximately normal distribution (Fig 4b). The average EC between different land uses was compared by using one-way ANOVA. The results showed that there is a significant difference in the soil salinity between uncultivated and agricultural area، but there is no significant difference between uncultivated and urban-industrial area. Soil salinity mean in the uncultivated area is at least three times higher than other land uses (Fig 3). The study of the spatial variability of EC is begun with the computation of directional variograms for EC in different directions. The best variograms was fitted in directions of 45 degrees، and a spherical theoretical covariance model fitted on variogram (Fig 6). The next step، cross validation of the prediction models computed with MSE and Pearson coefficient. MSE was minimum and Pearson coefficient was high (77%) and this parameter is identifier. Therefore valid variogram parameters (sill= 21000، rang= 0. 6 and nugget effect =0. 127) use for kriging and mapping (Table 2). According to the map of salt distribution in the top soil of study area (Fig 7)، all lands are saline (Ec> 4dS. m-1) but critical accumulation of salt is in the eastern region، especial Segzi plain. Segzi plain is located in the Eastern part of Isfahan province in the center of Iran and is about 40 kms from Isfahan center. The climate of the area according to the Gowsen method is found to be dry and semi-desert، respectively (Mojiri et al.، 2011). In Segzi plain are gypsum mines and wind can transport particles of chalk and sand that cause erosion and air pollution in Esfahan. Rainfall in east of Esfahan in comparison with center and west are lower and temperature is higher so there are more salt in compare to central and west parts. Moreover comparison of maps (salt distribution and land use) delineate، agricultural lands have lower salinity. Because irrigation and leaching are continued and transfer salt to deeper part. Moisture regime in study area is aridic. In this regime evapotranspiration is high and suction gradient transfer salinity solution to soil surface (Richards، 1954). The important result of this research was obtain concentration map of salt with this hope that this survey can be effective step in chose best decision in management of salinity lands to intention improvement and reformation this land. According result of this research، to be near with Lut deserts، low average rainfall، high annual temperature and high evaporation in eastern parts than western parts، are the most important parameters in accumulation of salt in this part of study area.

This study aims to describe and model the spatial variations of precipitation in the mountainous areas of middle Zagros using geo statistical techniques. To this aim, a dataset was obtained from 249 precipitation recording stations of Meteorological organization (climatologic, synoptic, and rain gauge) and Ministry of Energy of Iran in a 10-year period (1995-2004). Among these stations, 219 stations were used for modeling the spatial variability of precipitation, and the remaining 30 stations were used to evaluate the proposed method. To check the normality of data and detect the trend, Kolmogorov- Smirnov test and Trend Analysis of ESRI Arc GIS 9.3 is utilized, respectively. An evaluation of simple, ordinary, and general kriging with and without auxiliary variables of altitude, latitude, slope, and distance from ridges show that the ordinary kriging with latitude and distance from the ridge auxiliary variables are more precise. To illustrate the spatial variability of precipitation in the area of study, profiles of precipitation and altitudes perpendicular to ridges were plotted in the 50 Km intervals. These profiles make evidence that despite the relative correlation between precipitation and topography, the points with maximum precipitation does not correspond to the highest topographic points. Moreover, leeward and windward sides present different behaviors with respect to precipitation. Finally, it is found that topographies act as barriers in the way of air masses, thus air masses in clash with mountain release large amounts of their humidity.

The increasing use of gridded data in earth sciences or in hydrological estimations demands the application of mathematical approaches, especially geo-statistical ones, in obtaining accurate data. Since gridded data are generally generated from the interpolation of point data and/or processed satellite images, the use of neighborhood analysis is unavoidable in attaining more accuracy. On the other hand, in any neighborhood analysis, one is to be mindful of the selection and/or introduction of appropriate neighborhood window in terms of shape and dimension. As square or circular shapes (default shape) are usually used for neighborhood windows, an attempt has been made in the present study to address the acceptance condition of the default shape and to determine an appropriate window shape. To do so, use of image of surface semi-variogram (extracted from a geo-statistical approach) was proposed. The proposed approach was validated by determining the area of the plains in the Roodzard basin of Khozestan in Iran and extracting the channel network of Walnut Gulch Experimental Watershed (WGEW) in the State of Arizona in the United States. The findings of the study showed that the adoption of an elliptical shape for neighborhood window in extracting the Roodzard basin landform layer, led to 28 percent (93 Km2) change in the plain area extent in comparison to a square-shaped window, which will surely affect the scopes of terrain management of the basin. They also indicated that the use of a neighborhood window in WGEW led to 96 percent correspondence of the existing channel network with the extracted channel network in comparison to a circular (or circle-shaped) window.