JWSS - Journal of Water and Soil Science

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This study addresses the methodology of studying spatial variability of soil salinity. The information used is based on a semi-detailed soil survey, followed by a free survey, conducted in Ramhormoz, Khuzestan. The study of soil salinity variations was carried out using about 600 sampling points with an average distance of 500 m, at three depths of 0-50, 50-100, and 100-150 cm. To determine the spatial variability of soil salinity at different depths, the variogram which is a statistical function for the spatial variability analysis of the geographical variables was used. The results indicate that all variograms show almost the same range of 12 - 13 km which is closely related to the geographical distribution of the soil parent materials in the area. Ordinary block kriging was used to map salinity at different depths for a block dimension of 500 × 500 m. A comparison between the kriged estimates and the soil salinity map, produced during the soil survey, showed that the overall similarity between the test data and the classified kriging estimates was 40%, while the overall agreement between the test data and the soil survey salinity map was 36%. A detailed similarity calculation showed that the reliability of the classified kriging estimates representing the lowest salinity classes (S0, S1) is larger (75%) than the reliability of the soil survey salinity map representing these classes (50%). Consequently, the results indicate that geostatistical tools can be used to support the present-day procedures of soil salinity mapping.

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The analysis of the EC data set indicated that the spatial distribution of EC data of different depths are closely related to one another. It means that they are spatially cross correlated on one another and can be considered to be co-regionalized. It also implies that EC values at a particular depth contain useful information about the other depths which can be used to improve their estimation. In this research, we aimed to investigate the effects of using relevant ancillary information in the estimation procedure. To do this, cokriging was used. To evaluate this algorithm as a potential tool for mapping EC, its performance on the independent test data was evaluated and compared with the results obtained from studies using kriging. The results of the co-regionalization of EC at different depths indicated that cokriging the salinity data, although more rigorous from theoretical point of view, displayed no advantage over independent ordinary kriging at each depth. The results confirmed that cokriging improves little over ordinary kriging if the primary and auxiliary variables are almost equally sampled and all the variograms are identical. Also, ordinary kriging showed to be quite self-consistent since the predicted average salinity profile over the three depths was almost identical to the one predicted by cokriging. Considering the complexity of the cokriging and the LMC modeling, it is clear that there is no gain in using co-regionalization.

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Knowledge of the spatial dependency of soil properties, sensitive to grazing systems, is important from an ecosystem protection point of view. In the current study, geostatistical methods and fractal concepts have been used in order to characterize the impact of long-term grazing exclusion on the spatial variability of some soil chemical parameters including organic matter, total nitrogen, available phosphorous, and available potassium in both ungrazed and grazed sites located in Sabzkuh region, Chaharmahal Bakhtiari Province. The spatial pattern of different variables was studied using variogram. The results indicate that spatial variability of soil parameters depends upon grazing history. The variogram of organic matter in grazed areas shows a linear behavior without reaching the sill variance while organic matter in the ungrazed area established a strong spatial structure. Moreover, available phosphorous in the grazed area had pure nugget variation. Variograms of total nitrogen and available potassium in both grazed and ungrazed areas showed spatial structure approximated by spherical model. However, the range of variograms in exclusion sites was twice that of the grazed area. The results of applying the fractal theory show that soil properties have fractional behavior since increasing the scale of study reveals more details. Moreover, calculated fractal dimension values from variograms of different variables were close to 2.0 although this index was usually greater in the grazed area than in the exclusion sites. The calculated fractal dimensions can be used as a proper indicator of describing the pattern of spatial variability and its complexity.

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This study was conducted in a sugar beet field at Collage of Agriculture Experimental Station, Ferdowsi University of Mashhad, Iran. In order to describe the pattern of spatial variations and density of Chenopodium album, Solanum nigrum, Amaranthus sp., Portulaca oleracea, Echinochla crus-galli, and Convulvulus arvense as the main prevalent annual and perennial weeds of sugar beet fields, geostatistic methods were used. Samples were taken by systematic method from the corners of (7m × 7m) grids, using (0.5m × 0.5m) quadrates in three stages (before application of herbicides, after herbicide treatment, and before harvesting sugar beets). The integrity of spatial variation of variables was determined by using variogram functions and distribution maps of species. The variograms indicated that variations of all variables did not happen by chance. The maximum and minimum ranges of variation were observed in Solanum nigrum (by 142.7m) and Portulaca oleracea (by 1.5m), respectively. Both maximum and minimum ranges of variations were related to pre herbicide application. The highest and the lowest spatial correlations were related to Amaranthus sp. (in the third sampling treatment) and Solanum nigrum (in the first stage of sampling), respectively. The spatial distribution maps confirmed the patchiness distribution of the weeds. The patch of weed was constructed from a dense point at the center, gradually tapering toward the edges. The patches were skewed across the rows and irrigation channels. The structure of patches altered during the growing season. Any information on the distribution of weeds in the fields can be useful to improve decision makings in relation to applying the herbicides, selecting the herbicide type or applying the amount of herbicide. Also it can be useful to better design of weed control programs.

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Evaluating agricultural land management practices requires a thorough knowledge of soil spatial variability and understanding their relationships. This study was conducted at a traditionally operated wheat field in Sorkhankalateh district, located about 25 km northeast of Gorgan, in Golestan province, Iran. Soil samples of the 0-30 cm depth were collected right after planting at the end of autumn 2004 , 100 × 180m plot in a nested grid pattern (n=101). A 1 m² plot of wheat was harvested at each of 101 sites previously sampled at the end of spring. Statistical results showed that frequency distribution of all data was normal. The highest and lowest CV was related to grain yield (20.40%) and pH (0.59%) respectively. Variogram analysis showed that all parameters had spatial structure and the range values showed considerable variability among the measured parameters. The ranges of spatial dependence showed a variation from 23.99m for total N up to 93.92m for K. Among the parameters, total N and ESP had stronger spatial dependence while P had the lower spatial dependence. Interpolated maps of Kriging demonstrated that crop and soil properties did not have a random pattern but had a spatial distribution. The spatial distribution of total N was similar to organic matter and also there was similarity between spatial distribution of harvest index and available P. The results demonstrated that, the spatial distribution and spatial dependence level of soil properties can be different even within similarly managed farms. Variography and Kriging can be useful tools for designing soil sampling strategies, characterizing management zones and variable application rates of inputs in the precision agriculture.

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To investigate spatial variability of soil characteristics in the most valuble forest stands in the northern Iran using geostatistical approach, a twenty hectare area in Experimental Forest Station of Tarbiat Modares University was considered. Soil samples were taken from pits, mounds, canopy gaps, under single trees and closed canopy positions at 0 - 15, 15 - 30 and 30 - 45 cm depths using core soil sampler (81cm2 cross section). pH and organic matter were measured in the laboratory, and then carbons to nitrogen ratio and carbon sequestration were calculated. Spatial variability for soil characteristics revealed anisotropic variogram due to the same variability of surface variogram in different depths. pH in all the depths and carbon sequestration in 30 - 45cm depth showed exponential model whereas the other soil characteristics resulted in a linear model in different layers. The results of spatial structure showed pH with medium structure in all the depths, organic matter and carbon to nitrogen ratio with pure nugget effect (non structure), carbon sequestration in 0 - 30cm depth with weak structure and 30 - 45cm depth with medium spatial structure.

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Trends in groundwater pollution with nitrate and phosphate may be an indication of water resources management. The aims of this research were to determine changes in nitrate and phosphate concentration and changes in spatial variability patterns of nitrate and phosphate and distribution over a 5-year period. To do this, 100 agricultural wells were sampled in the years 2006, 2010 and 2011, and analyzed for nitrate and phosphate concentrations. From 2006 to 2011, the mean nitrate concentration increased from 18 to 27 mg/L and the mean phosphate concentration from 0.05 to 0.15 mg/L. Spatial patterns did not change, and spherical model described the patterns throughout this period. Maps showed that the nitrate and phosphate concentrations are higher in the south, and lower in the north of the aquifer. It seems that the presence of the municipality treatment plant, intensive cattle farming, shallower water-table and inward flow gradient may be the reasons for the higher concentration in the southern part of the aquifer. From the comparison of the maps, it was clear that the areas of less polluted classes had shrunk while the areas of more polluted classes had grown from 2006 to 2011.

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Shahrekord groundwater is the main source of water for drinking, and the agricultural and industrial activities of its inhabitants. Water quality measures of scaling and corrosion can deteriorate steel-based systems used for storage or supplying water for drinking and to industry and irrigation. The main aim of this study was to assess the spatial variability and mapping of scaling and corrosion using Langelier index (LI) and Ryznar index (RI) and that of the related parameters of pH, total dissolved solids (TDS), total hardness (TH), and total alkalinity (TA) in Shahrekord groundwater. For this purpose, water samples from 97 wells were analyzed for pH, TDS, TH, and TA and LI and RI indices were calculated. The Gaussian model best described the spatial variability of TDS while the Spherical model was best for all other parameters. Based on LI and RI averages of, -0.13 and 7.9 respectively, Shahrekord groundwater has a slight potential for corrosion. The values of all parameters, except RI, were lowest in the northwest and highest in the southeast of the aquifer. In most parts and in the center of the aquifer, the values of LI ranged from -0.5 to zero indicating negligible scaling potential. Spatial distribution of the RI index was almost inversely symmetrical to that of LI index. LI showed strong positive correlations with its components (varying from 0.61 to 0.90) while RI had strong negative correlations with its components (ranging from -0.66 to -0.98). LI and RI had the strongest correlations, respectively, with pH (r=0.90) and total alkalinity (r=-0.90).

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Continuous decline of groundwater quality for agricultural purposes has become a major concern in extensive arid and semi-arid regions. Therefore, mapping the quality of groundwater on a broad scale is an essential step in land management. This study aimed to map spatial distribution of two important groundwater quality indices including EC and SAR in Isfahan province, Iran, using geostatistical techniques. Different techniques such as Kriging, IDW and RBF were applied to water quality data of 540 groundwater wells to map continuous variations of the EC and SAR indices in Arc GIS 9.3 environment. Among the interpolation methods, the Kriging by circular variogram model performed best and had the lowest RMSe error. Therefore, the produced maps from this technique were classified based on Wilcox method. Results showed that EC varies across the province from 392.2 in the west to about 17917.6 µmmhos/ cm in the northwest and eastern parts of the study area. The highest and lowest SAR values were estimated in the towns of Khour va Biabanak and Semirom, ranging from 38.9 to 0.13, respectively. According to the map of irrigation water quality based on Wilcox method, about 12.13 % of the region was classified as good, 16% as moderate, 17.5% as unsuitable and 54.35% as unusable category. In general, the quality of groundwater in Isfahan province decreases from west to east and also from south to north, especially in playas (non-agricultural lands) where the unusable class is dominant.

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Any change in the characteristics of air, soil, water and food that adversely affect the health of the ecosystem, activities of human and other organismsis called contamination. Heavy metal uptake by plants depends on the type and concentration of metalin soil, its bioavailability, and plant species. The use of new sciences such as geostatistics is useful for fast and simple determination of soil and leaf contamination risk. This study studied the amount of soil and leaves of Platanus orientalis contamination in order to map the lead (Pb) and cadmium (Cd) concentration in Rasht city using a geostatistic method. To achieve the goal, 126 samples of surface soil (0-30 cm) and 76 leaf samples (Platanus orientalis) were collected from city streets. Total concentrations of lead and cadmium in the soils and leaves were determined, and clay, silt and sand particle percentage, organic matters, and soil pH were measured. Average concentrations of elements in terms of mg/kg were as follows: soil’s Lead: 86.62, soil’s Cadmium: 0.6, leaf’s Lead: 8.99. For soil Pb and Cd and leaf Pb, spherical model yielded a better fit in the experimental variogram in GS+ program by using trial and error method. According to the spatial structure, Kriging and IDW estimators were used for interpolation. Kriging estimation was mapped using Arc GIS 9.2 software.

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