Mahsa Sameti; Seied Hosein Sanaei-Nejad; Firoozeh Rivaz; Bijan Ghahraman
Abstract
Introduction: Drought is a very complex natural phenomenon which changes with time and space. Spatial and temporal variations of drought are analyzed separately. Geostatistical methods can be used for spatiotemporal analyses to find related spatial and temporal pattern changes. These methods, ...
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Introduction: Drought is a very complex natural phenomenon which changes with time and space. Spatial and temporal variations of drought are analyzed separately. Geostatistical methods can be used for spatiotemporal analyses to find related spatial and temporal pattern changes. These methods, which use the spatio-temporal data, considering the spatial position of the data relative to each other, also take into account their temporal dependence. If needed, they can estimate values of their variable at any location and any time. Moreover, the drought spatial variations in the studied region can be drawn at every desired period. On the other hand, it is expected that intervening of the time dimension in the equations of these methods, as compared to the purely spatial methods, provide more precision in estimating the values of drought indices, which is studied in this research.
Materials and Methods: Monthly rainfall data of 48 stations in the northeast of Iran for the period of 1981-2012 were used in this study. The SPI drought index is calculated for the 12-month time scale. Data were divided into two groups of training data from 1981-2011 and experimental data of 2012. After analyzing the data regarding their stationarity and isotropic assumptions, the spatiotemporal data were formed and their spatiotemporal empirical variogram was drawn. Furthermore, the purely spatial and temporal variograms for the zero space and time steps were also drawn. Then, four models of the spatiotemporal variogram functions were applied on the training data. The performance of these models was tested and compared by estimating the parameters of the model based on the Square Error (MSE). Moreover, three-dimensional fitted variograms were drawn for different models. Mean The best spatiotemporal variogram model was selected by comparing the models prediction with experimental data using the Mean Square Prediction Error (MSPE). Using spatiotemporal kriging method, the predicted values of experimental data were interpolated and that of the observed values were interpolated by kriging method. Cross validation on experimental data was also performed using RMSE, MAE, ME and COR. Then spatiotemporal and purely spatial variogram models were investigated and compared.
Results and Discussion: The results showed that the 12-month SPI index had no spatial trend but had a decreasing trend against the time. Hence, a simple regression equation was used for fitting the trend of the data. After detrending the data, the SPI index values were considered as the dependent variable, while the time was taken as the independent variable. On the other hand, drawing the variogram in different directions (0°, 45°, 90°, and 135°) had no significant effect relative to each other, and the hypothesis of isotropic state was accepted. The plots of purely spatial and temporal variograms showed that the spherical variogram for space and the linear variogram for the time would have the best fitting. The empirical 3-D and 2-D spatiotemporal variograms of the training data were plotted. The empirical 3-D variogram showed that the data had reached to its temporal sill in a 1-year time lag, and had reached to its spatial sill, in about 25-kilometers, which are in conformity with the purely spatial and temporal variograms. The comparison of different variogram functions showed that the MSE values of the separable, metric, product-sum and sum-metric models were 0.00139, 0.00295, 0.00111, and 0.00112, respectively, the last two of which had fewer errors. Drawing the spatiotemporal variogram of these functions showed that the spatiotemporal variogram of product-sum and sum-metric models have more similarity to the sample one. Regarding the selection of the best model, the MSPE statistics of the product-sum and sum-metric models were 0.281 and 0.389, respectively. Therefore, the product-sum model could be selected as the best model. The least rate of errors was found in the exponential variogram model for space, and in the linear variogram for the time. The parameters of the nugget effect, partial sill and range for the spatial variogram would be 0.00, 0.063, and 5.78, and for the temporal variogram would be 0.00, 0.635, and 1.044, respectively. After predicting values of 12-month SPI in 2012 by the product-sum variogram model and adding the values of the trend, they were interpolated by using the spatiotemporal kriging, and the observed values were interpolated by the use of kriging. The obtained plot from the predicted values had great similarity with that of the observed values, which indicates the appropriate capability of the model in predicting the unobserved values. The cross-validation of different spatiotemporal and the spatial models with 25 and 47 neighborhoods showed that the performance of the models had no significant differences relative to each other, and they also had no better performance relative to the purely spatial model.
Conclusion: The results of this study showed that the product-sum model had a better performance among different spatiotemporal variogram models in predicting the 12-month SPI values of 2012. However, the performances of different spatiotemporal models were quite close to each other. There is no significant difference that could be observed between spatiotemporal and purely spatial models. Also, it is proposed to use the dynamic spatiotemporal models and the results to be compared with the classical models.
Morteza Bahmani; jahangard mohammadi; Isa Esfandiarpour Borujeni; Hamidreza Mottaghian; Keramatollah Saeidi
Abstract
Introduction: The importance and the presence of spatial variability in soil properties is inevitable, however, the understanding of causes and sources of the variability is not complete. Spatial variation of soil attributes can affect the quality and quantity of plants. Investigation of the soil variability ...
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Introduction: The importance and the presence of spatial variability in soil properties is inevitable, however, the understanding of causes and sources of the variability is not complete. Spatial variation of soil attributes can affect the quality and quantity of plants. Investigation of the soil variability at the small scale can be evaluated by classic statistics and geospatial statistics. The present study was conducted to investigate the spatial variability of yield characteristics of rose (Rosa Damasceneea Mill) and soil characteristics in two main cultivated fields of rose (Negar- Golzar) with different climatic and topographic characteristics located in Bardsir city, Kerman Province.
Materials and Methods: In order to achieve the objectives of the present study, 100 soil and plant samples were collected from each farm. The soil samples were taken from 0 to 25 cm depth and analyzed. The measured soil properties at each location were including fragment, clay, silt, sand, and organic matter contents, CEC, calcium carbonate equivalent, EC, pH, total nitrogen, available phosphorus, and available potassium. Moreover, some plant characteristics (yield, plant height, and plant crown diameter) were measured at each point. Then, maps of soil properties and plant induces were prepared using Geoeas, Variowin, and surfer software. Descriptive statistics were applied using Statistica software (version 20). Kolmogorov-Smirnov test was also used to test the tolerance of variables distribution.
Results and Discussion: The results of Kolmogorov-Smirnov test showed that all characteristics of the plant and soil in both farms follow the normal distribution. Statistical analysis showed that coefficient of variation of soil properties was as follows: total nitrogen (54.47%) and pH (3.16%) in Negar farm, and EC (46.09%) and pH (35.3%) in Golzar farm. The variability of nutrients in both farms had similar trends, so that total nitrogen, phosphorus and potassium have the highest to lowest coefficients of variation, respectively. Analysis of variograms indicated that all of the variables in both fields have a strong and moderate spatial variability. Ranges for variograms were from 122.16m (for yield) to 218.46 m (for silt) in Negar farm and from 115.1m (for available K) to 228 m for (total nitrogen) in Golzar unit. The distribution conditions and spatial variations of the soil properties in the study area were not uniform due to variation of the range of the variograms. The results also showed that the yield characteristics of the rose with some soil characteristics have a closer spatial relationship. About this, in the Negar farm, the range of the rose flower yield was close to the clay, available potassium and calcium carbonate contents. In the Golzar farm, the range of rose flower yield was close to the range of clay, silt, fragments and available phosphorus contents. The spatial correlation ratio showed that all plant characteristics including plant yield, plant height and plant diameter had a strong spatial correlation in the Golzar farm, and all characteristics of the soil were in the medium spatial correlation. Also, in the Negar farm, the product yield characteristics were in a strong spatial correlation class, and all other characteristics were in the medium spatial correlation. Kriging maps showed that soil characteristics and product yield in the study area had spatial distribution. The similarity of the spatial distribution pattern of some variables was one of the important features that these maps showed.
Conclusion: The results of this study showed the characteristics of plant yield and soil characteristics have a moderate to strong spatial dependency even in small scales. Kriging maps illustrated that the pattern and distribution of soil properties even within a farm can be varied. However, the spatial pattern of some soil characteristics such as organic matter and total nitrogen with the spatial pattern of plant characteristics and the dimensions of the farms showed conformity. This indicates that the variability of these characteristics is mainly under the management of farmers, and in order to optimize the use of nutrients, inputs should be re-evaluated by farm managers. In general, the results of this study indicated geostatistical method can be used to recognize of control factors of plant production and use its information in order to improve management.
farrokh asadzadeh; Kamal Khosraviaqdam; Nafiseh Yaghmaeian Mahabadi; Hassan Ramezanpour
Abstract
Introduction: Soil texture is the average size of soil particles which depends on the relative proportion of sand, silt and clay contents. Soil texture is one of the most important features used by soil and environmental scientists to describe soils. Soil texture directly affects the soil porosity, which ...
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Introduction: Soil texture is the average size of soil particles which depends on the relative proportion of sand, silt and clay contents. Soil texture is one of the most important features used by soil and environmental scientists to describe soils. Soil texture directly affects the soil porosity, which in turn, determines water-retention and flow characteristics, nutrient-holding capacity, internal drainage, sorption characteristics and long-term soil fertility. High-resolution soil maps are essential for land-use planning and other activities related to forestry, agriculture and environment protection. Given the soil texture roles in controlling the soil functions, it is necessary to understand the spatial distribution of this feature in regional scale. As soil texture is a staticproperty, regional scale soil texture maps can thus help environmental scientists to predict different soil-related processes. The objective of this study was to develop a soil textural class map using Terra satellite MODIS sensor images.
Material and Methods: To achieve this goal, the digital elevation model SRTM radar of the studied area for soil samples from different altitudes and slopes was prepared in foursen consecutive 30 meters time frame. The nearest neighbor method with an error of less than 0.5 pixels was used and the elevation layers were mosaicked and transmitted to the UTM ZON-38 coordinate system and GIS Ready Became. The normalized difference vegetation index of bands 1 and 2 of the matrix was obtained to isolate the reflection of the electromagnetic spectrum of vegetation and soil. This final mosaicked digital elevation model was then divided into different altitudes to accurately evaluate the surface texture. The 60 spatial points were selected to estimate the texture of surface soil in thestudied area with systematic randomized sampling. In the current study, soil texture was determined forthe air-dried samplesusing hydrometer. The SWIR bands of MODIS with resolution of 500 meters were selected for sampling dates. After corrections, DN values of the bands for sampling points were extracted. The Pearson correlation coefficient and step wise regression techniques were used to establish proper relationships between the DN values of the SWIR bands and the soil particles. Kriging and cokriging methods were also employed to create a spatially distributed map of the soil textural classes.
Results and Discussion: The results showed that there is a close correlation between the SWIR bands of the terra satellite and the MODIS sensor with band 3, and using this auxiliary variable significantly reduces the estimation error. The best model for fitting semivariogram for clay, silt and sand contents were spherical, spherical and exponential models and the best fitting Cross-semivariogram for clay, silt and sand contents were spherical, exponential and exponential models, respectively. The highest and lowest error estimation was, respectively, related to sand and clay content. The maximum and minimum decrease of estimation error by the auxiliary variables was found for sand and clay content, respectively. The nugget/sill ratio of the kriging semivariograms was greater than 25%for sand and claycontentand lower than 25%for sand and silt content. This indicates that sand and silt contents had a strong spatial dependency, and clay content hada moderate spatial dependency. These ratios for cokriging cross-semivariograms of sand, silt and clay contentsware less than 25%. The interpolation of estimated soil texture was also determined using the cokriging method with 70% of the soil texture measured in the laboratory.
Conclusions: Our results indicated thatcokriging method estimated the soil particles more accurately as compared with linear multi-variable stepwise regression and kriging methods. Application of cokriging method also reduces the number of sampling points and the estimation error of soil texture zoning. Therefore, cokriging method seems to be better suited in impact assessments for data-scarceregions such as Iran.
Elham Afzali Moghadam; naser boroumand; vahidreza jalali; saleh sanjari
Abstract
Introduction: The hydraulic parameters are very important for perception of water flow in unsaturated soil and using pollutants and nutrient flow modeling in the soil. The effect of soil management and land uses on soil parameters can directly alter soil hydraulic parameters. Because of interactive and ...
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Introduction: The hydraulic parameters are very important for perception of water flow in unsaturated soil and using pollutants and nutrient flow modeling in the soil. The effect of soil management and land uses on soil parameters can directly alter soil hydraulic parameters. Because of interactive and tight relationship between soil and plant covering, studying the soil parameters and its changing during different land uses is vital. The main object of this study was evaluating the effects of different land uses on soil saturated hydraulic conductivity.
Materials and Methods: This study was performed in about 100 hectare fields of Khezrabad region in the 25 km south of the Jiroft county located in south eastern of Kerman province. The region gridded into 1000×1000 meter grids with use of Google earth and Arc GIS software, sampling places has been selected in the center of each grid. Measurement of soil saturated hydraulic conductivity done with the Guelph permeameter in the center of each grid. For the measurement of physical parameters such as bulk density, percent of sand, silt, clay in the laboratory, sampling done from 30cm depth so samples transferred to the laboratory. In this study in order to ensure the normal distribution of variables, the Kolmogorov-Smirnov test has been used with SPSS14 software. The Kriging method was used for interpolation and providing spatial maps.
Results and Discussion: Agriculture, garden and sterile lands were selected for the object of the present study. The study area includes garden, agriculture and sterile lands at the same time. The study area contains 3 classes of soil texture as: sandy, sandy-loamy and loamy-sand. The results showed that soil saturated hydraulic conductivity (ks) with strong spatial correlation had a high spatial variability. The fluctuation ranges of its values changes from 0.02 to 2325.71 cm per hour. The lowest value of ks was observed in garden land (by having the lowest value of soil bulk density) while the highest value was observed in sterile land (by having the highest value of soil bulk density). The results also showed that semi-variogram of garden, agriculture and sterile land were not the same, and it may gain from different types of agricultural operations, type of land use and various textures so that from garden land to sterile land, the soil texture becomes lighter and level of saturated hydraulic conductivity changes completely different. Several reasons maybe considered including soil different structures due to different type of agricultural operations and type of cultivation for every single land use. The change process of saturated hydraulic conductivity for garden and agricultural land was identical and for both the Gaussian model were fitted. According to the nugget effect ratio to the sill (C0/C0+C), variability of saturated hydraulic conductivity in agricultural land has a stronger spatial correlation (0.0006) and also has a higher radius of effect range (11740m) compared to garden land in which the ratio of the nugget effect ratio to sill is 0.28 and its radius of effect range is 8030 meters. the radius of effect range in sterile land had the lowest value among studied land uses, though having strong correlation, the effect range of this correlation is low and, compared to other lands, the changes process was more randomly obtained. To mention the reasons of this finding it is possible to refer to area of the sterile land, dispersion of the sampling points and long distance between pair points. The lowest spatial correlation belonged to garden land with middle spatial correlation class and the reason can be explained as due to increase of sand, decrease of clay and silt, bulk density of soil increases as well and leads to increase of coarse pores and consequently increasing saturated hydraulic conductivity of soil.
Results showed that soil saturated hydraulic conductivity (ks) with strong spatial correlation has high spatial variability and these variability consist lowest quantity in the garden lands and highest quantity in the sterile lands. The distribution pattern of Ks was seen similar to the sand and the soils bulk density, this pattern was opposite to the clay distribution pattern, this indicates the effect of soil physical parameters on saturated hydraulic conductivity.
Conclusion: According to the evaluation parameters CRM, MAE and MBA, Gaussian model is the best fitted model to soil saturated hydraulic conductivity data and soil parameters such as saturated hydraulic conductivity consist spatial variability related to sampling scale. The factors of land type and consequently type of land cultivation, lands management system, type of agricultural operations, soil particles size and bulk density of soil have the most impact on variability of Ks.
Sheyda Kaboodi; farzin shahbazi; Nasser Aliasgharzad; nosratola najafi; naser davatgar
Abstract
Introduction: Understanding soil biology and ecology is increasingly important for renewing and sustainability of ecosystems. In all ecosystems, soil microbes play an important role in organic matter turnover, nutrient cycling and availability of nutrients for plants. Different scenarios of land use ...
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Introduction: Understanding soil biology and ecology is increasingly important for renewing and sustainability of ecosystems. In all ecosystems, soil microbes play an important role in organic matter turnover, nutrient cycling and availability of nutrients for plants. Different scenarios of land use may affect soil biological properties. Advanced information technologies in modern software tools such as spatial geostatistics and geographical information system (GIS) enable the integration of large and complex databases, models, tools and techniques, and are proposed to improve the process of soil quality and sustainability. Spatial distribution of chemical and biological properties under three scenarios of land use was assessed.
Materials and Methods: This study was carried out in Mirabad area located in the western part of Souldoz plain surrounded by Urmieh, Miandoab, Piranshahr and Naghadeh cities in the west Azerbaijan province with latitude and longitude of 36°59′N and 45°18′E, respectively. The altitude varies from 1310 to 1345 with average of 1325 m above sea level. The monthly average temperature ranges from -1.4 °C in January to 24.6 °C in July and monthly precipitation ranges from 0.9 mm in July to 106.6 mm in March. Apple orchard, crop production field and rich pasture are three selected scenarios in this research work. Soil samples were systematically collected at 65 sampling points (0-30 cm) on mid July 2010. Soil chemical and biological properties i.e. microbial community, organic carbon and calcium carbonate equivalent were determined. The ArcGIS Geostatistical Analyst tool was applied for assessing and mapping the spatial variability of measured properties. The experimental design was randomized complete blocks design (RCBD) with five replications. Two widely applied methods i.e. Kriging and Inverse Distance Weighed (IDW) were employed for interpolation. According to the ratio of nugget variance to sill of the best variogram model three following spatial dependence conditions for the soil properties can be considered: (I) if this ratio is less than 25%, then the variable has strong spatial dependence; (II) if the ratio is between 25% and 75%, the variable has moderate spatial dependence; and (III) otherwise, the variable has weak spatial dependence. Data were also integrated with GIS for creating digital soil biological maps after testing analysis and interpolating the mentioned properties.
Results and Discussion: Spherical model was the best isotropic model fitted to variograms of all examined properties. The value of statistics (R2 and reduced sum of squares (RSS)) revealed that IDW method estimated calcium carbonate equivalent more reliably while organic carbon and microbial community was estimated more accurately by Kriging method. The minimum effective range (6110 m) was found for microbial community which had the strong spatial dependence [(Co/Co+C)
Saeideh Bardsirizadeh; Isa Esfandiarpour Borujeni; Ali Asghar Besalatpour; Peyman Abbaszadeh Dahaji
Abstract
Introduction: Aggregate, as the basic unit of soil structure,represents a collection of primary particles which their adherence to each other is more than their connection to environ soilparticles. Aggregate stability is a highly complex parameter influencing a wide range of soil properties, including ...
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Introduction: Aggregate, as the basic unit of soil structure,represents a collection of primary particles which their adherence to each other is more than their connection to environ soilparticles. Aggregate stability is a highly complex parameter influencing a wide range of soil properties, including carbon stabilization, soil porosity, water infiltration, aeration, compatibility, water retention, hydraulic conductivity andresistance to erosion by water and overland flows. Maintaining high stability of soil aggregate is essential for preserving soil productivity, minimizing soil erosion and degradation and thus minimizing environmental pollution as well. Nevertheless, aggregate stability is described as one of the soil properties that can serve as an indicator of soil quality.The main purpose of this study is to determine the most important component of soil aggregate (macro and/ormicro) in estimating the soil structural stability in the Rabor region, Kerman province, using geostatistical method.
Materials and Methods: Ninetysurface soil samples (0 to 10 cm) were taken on a 200 m square sampling grid in the study area for the geostatistical studies.After air drying the soil samples and passing them through a 4 mm sieve, the percentage of aggregates belong tothree parts of total, macro, and micro classes and aggregate staility were calculated in both dry and wet conditions.Some stability indices were calculated and their spatial variabilities were investigated using two variography and estimation stages methods. Finally, the kriged map of each aggregate stability indicator was produced. To determine the compatibility of kriged maps of the soil aggregates stability indices calculated for the macro and micro aggregates with aggregates stability index (i.e., AS index) calculated forthe total aggregates, the overall accuracy related to each aggregate component (i.e., macro and micro) was calculatedafter creating an error matrix.
Results and Discussion: The results showed that total aggregate stability in the dry condition and macro aggregate stability in the wet condition had the lowest and highest coefficients of variability,respectively. The highest percentage of total aggregate stability (i.e., 89.90 %)was observed in the north and southeast positions of the study areain the dryconditionwhich had the highest amount of organic matter(i.e., 2.30 %). Also, the variograms of all investigated variables were exponentially and their ranges were varied between 380 to 450 m. Although the obtainedranges were different, a sampling distance more or less equal to 420 m is reasonable to study the most of the variables in the area. This can be a good indicator to decrease the sampling tasks for monitoring of these parameters in future.An overall look at the obtained root mean square standardized error (RMSSE) values indicated a high correlation between the measured and estimated values of all investigatedvariables (except for macro aggregate stability in the wet condition). However, all variables had a strong spatial correlation class. The percentage of overall accuracy obtained from crossing the total and macro aggregate kriged maps in the dry condition (i.e., 51.75 %), was more than its percentage for similar maps in the wet condition (i.e., 32.17 %). In return, the percentage of overall accuracy obtained from crossing the total and micro aggregate kriged maps in the wet condition (i.e., 17.31 %)was greater than its percentage for the mentioned maps in the dry condition (i.e., 10.93 %). Because of macro aggregate sensitivities to the amount of pressure imposed on them (as in the wet sieving method, the aggregates are under pressure caused by water energyin addition to tensions related to mechanical motion of sieving), the conformities of above two mentioned maps were less than those in the dry sieving method.
Conclusions: In general, the soil aggregates stability depends strongly on the amount of pressure imposed on them. Besides, the study of spatial variability of macro and micro aggregate stabilities and relative effects of each on the soil structure stability can be useful for choosing proper land management activities in future studies. According to theeffect of aggregation on nutrient cycling, capture, storage and water movement, and also other soil characteristics affecting plant growth and sustainable agriculture on one hand, and the effect of organic matter on aggregation on the other hand, it can be concluded thatall human activities that have a role in reducing or removing organic matter from the soil (e.g., grazing, deforestation, and intensive cultivation etc.) may reduce soil aggregate stability and finally can jeopardize human life in a near future.
Leila Bakhshandehmehr; Mohammad Reza Yazdani; Ali-Asghar Zolfaghari
Abstract
Introduction: In recent years, due to the reduction in surface water, utilization of groundwater has been increased to meet the growing demand of irrigation water. The quality of these water resources is continually changing, due to the geological formations, the amount of utilization, and climatic parameters. ...
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Introduction: In recent years, due to the reduction in surface water, utilization of groundwater has been increased to meet the growing demand of irrigation water. The quality of these water resources is continually changing, due to the geological formations, the amount of utilization, and climatic parameters. In many developing countries, the irrigation water is obtained from poor quality groundwater resources, which in turn, creates unfavorable circumstances for plant growth and reduces the agricultural yield. Providing adequate water resources for agricultural utilization is one of the most important steps needed to achieve the developmental targets of sustainable agriculture. Thus, this necessitates the assessment and evaluation of the quality of irrigation water. There are many proposed methods to determine the suitability of water for different applications, such as Piper, Wilcox, and Schoeller diagrams. Zoning of quality and suitability of irrigation water could represent the prone and critical areas to groundwater exploitation. Garmsar alluvial fan is one of the most sensitive areas in the country where traditional agriculture practices had turned into modern techniques and excessive exploitation of groundwater has caused an intensepressure on aquifers and increased water salinity. The aim of this study is to evaluate the suitability of groundwater for irrigation in a 10-year period (2002-2012) and its changes in this basin.
Materials and Methods: Garmsar alluvial fan is located in the North-West of Semnan Province. Semnan is situated in the Southern hillside of the Alborz Mountains, in North of Iran. The study area includes the agricultural land on this alluvial fan and covers over 3750 hectares of this basin. In order to evaluate the quality of groundwater in this area, the electrical conductivity and sodium absorption ratio of 42 sample wells were calculated. The raster maps of these indicators were obtained using Geo-statistical techniques. The suitability of irrigation water was determined by Wilcox diagram. Upon evaluating the data distribution and testing the data from Klomogrov-Smirnov normality test, normalization of the data was performed in SPSS software. Spatial correlation and spatial structure of variables were analyzed by drawing their semi-variograms in GS+ software. The most accurate variogram model was selected according to the lowest Residual Sums of Squares (RSS) and the highest correlation coefficient (R2). Interpolation and zoning of the indicators were performed in ArcGIS software and the Quality classes were determined.
Results and Discussion: According to the results of Kolmogorov-Smirnov test, none of the data series had normal distribution. Therefore, they were normalized through calculating the logarithm of variables. Fitting and the selection of variograms were performed in GS+ software and after the calculation of errors, kriging method with Guassian model was determined as the best fitting model. The correlation coefficient was 0.896 for electrical conductivity and 0.99 for sodium absorption ratio. Interpolation of indicators in ArcGIS implied fewer measurements of these indicators in north of the study area (Hableh-Rood inlet). The maximum measurement of indicators was observed on the western edge of the alluvial fan. In total, the values of both electrical conductivity and a sodium absorption ratio indicators in the western half of the area, in the vicinity of the third period domes, were more than the eastern half. The result of the water classification using Wilcox diagram represented the unsuitability of groundwater for irrigation in all of the study area. The area with unusable groundwater for irrigation has increased over the 2005 – 2009 period.
Conclusion: In this study, relying on the use of GIS and Geo-statistical methods, the quality of Garmsar basin groundwater has been evaluated. The electrical conductivity was applied to monitor water salinity, and Sodium absorption ratio was used to monitor alkalinity. The interpolation of these indicators was performed by Kriging method and Guassian fitting model. Likewise, in other studies, the Kriging method was introduced as an appropriate method for the interpolation of chemical parameters of the groundwater. The accuracy of various fitting models in the prediction of interpolated values differed according to the number and the distribution of sample points. In the current study, the Guassian fitting model was determined as the best model to interpolate both of the indicators. According to the maps, it seems that the third period domes in the western margin of the study area have a great influence on the quality of Garmsar’s surface water and groundwater. In total, the groundwater of Garmsar basin didn’t poss high suitability for irrigation, and was classified into two unsuitable and unusable classes. Moreover, according to the maps, the maximum area of unusable groundwater for irrigation in the area was observed in 2008.
hojjat ghorbani vaghei; M. Davari
Abstract
Introduction: Soil organic carbon (SOC) has great impacts on soil properties, soil productivity, food security, land degradation and global warming. Similar to other soil properties, SOC has a strong spatial heterogeneity as a result of dynamic interactions between parent material, climate and geological ...
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Introduction: Soil organic carbon (SOC) has great impacts on soil properties, soil productivity, food security, land degradation and global warming. Similar to other soil properties, SOC has a strong spatial heterogeneity as a result of dynamic interactions between parent material, climate and geological history, at both regional and continental scales. However, landscape attributes including slope, aspect, altitude, and land use types are dominant factors influencing on SOC in areas with the same parent materials and climate regime. Understanding and identifying the spatial and temporal distribution of SOC is essential to evaluate soil quality, agricultural management, watershed modeling and soil carbon sequestration budgets. Therefore, the objectives of this study was to estimate soil organic carbon content in the Aligodarz watershed, and to investigate the effects of altitude, slope, and land use type on SOC.
Materials and Methods: The research was carried out in the Aligodraz watershed in Lorestan province of Iran. The study area is located between latitudes N 33° 10' 51.72"to N 33° 34' 28.22" and longitudes E 49° 27' 17.99"to E 49° 58' 40.84" 14 that covers an area of 1078.9 km2. It has an altitude between 1866.3 and 3200 m above sea-level. The primary land uses within the watershed include pasture, dryland and irrigated farming. In this study, soil samples were randomly collected from 206 sites at depth of 0– 15 cm during June and August 2003. The mean distance between samples was about 5 km. Soil samples were air-dried in the shade for about 7 days and then passed through a 0.25 mm prior to determination of SOC. Soil organic carbon content was determined in triplicate for each sample using the Walkey-Black method. Basic statistical analyses for frequency distribution, normality tests, Pearson's correlation and analysis of variance were conducted using SPSS (version 18.0). Calculation of experimental variograms and modeling of spatial distribution of SOC were carried out with the geostatistical software GS+ (version 5. 1). Maps were generated by using ILWIS (version 3.3) GIS software.
Results and Discussion: The results revealed that the raw SOC data have a long tail towards higher concentrations, whereas that squareroot transformed data can be satisfactorily modelled by a normal distribution. The probability distribution of SOC appeared to be positively skewed and have a positive kurtosis. The square root transformed data showed small skewness and kurtosis, and passed the K–S normality test at a significance level of higher than 0.05. Therefore, the square root transformed data of SOC was used for analyses. The SOC concentration varied from 0.08 to 2.39%, with an arithmetic mean of 0.81% and geometric mean of 0.73%. The coefficient of variation (CV), as an index of overall variability of SOC, was 44.49%. According to the classification system presented by Nielson and Bouma (1985), a variable is moderately varying if the CV is between 10% and 100%. Therefore, the content of SOC in the Aligodarz watershed can be considered to be in moderate variability. The experimental variogram of SOC was fitted by an exponential model. The values of the range, nugget, sill, and nugget/sill ratio of the best-fitted model were 6.80 km, 0.058, 0.133, and 43.6%, respectively. The positive nugget value can be explained by sampling error, short range variability, and unexplained and inherent variability. The nugget/sill ratio of 43.6% showed a moderate spatial dependence of SOC in the study area. The parameters of the exponential smivariogram model were used for kriging method to produce a spatial distribution map of SOC in the study area. The interpolated values ranged between 0.30 and 1.40%. Southern and central parts of this study area have the highest SOC concentrations, while the northern parts have the lowest concentrations of SOC. Kriging results also showed that the major parts of the Aligodarz watershed (about 87%) have statistically SOC content less than 1%. Lower SOC concentrations were associated with high altitude (r = −0.265**). The results of Pearson correlation analysis showed that soil organic carbon content has a significantly negative correlatiton with slope gradient (r = −0.217**). The results also indicated that the SOC content was variable for the different land use types. The irrigated lands had the highest SOC concentrations, while the pasture lands had the lowest SOC values.
Conclusion: The square-root transformed data of SOC in Aligodarz watershed of Lorestan province, Iran, followed a normal distribution, with an arithmetic mean of 0.81%, and geometric mean of 0.73%. The coefficient of variation and nugget/sill ratio revealed a moderate spatial dependence of SOC in the study area. The results indicated that the major parts of the Aligodarz watershed have SOC content less than 1%. The land use type had a significant effect on the spatial variability of SOC and that lower SOC concentrations were associated with higher altitude and slope gradients. The irrigated and pasture lands had the highest and lowest SOC concentrations, respectively.
Sh. Asghari; S. Dizajghoorbani Aghdam; Abazar Esmali
Abstract
Knowledge of the spatial distribution of soil properties is the major issues in identifying, program planning, management and utilization of soil and water resources. This study was carried out to investigate the spatial variability of some important soil physical quality indices including sand, silt, ...
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Knowledge of the spatial distribution of soil properties is the major issues in identifying, program planning, management and utilization of soil and water resources. This study was carried out to investigate the spatial variability of some important soil physical quality indices including sand, silt, clay, mean weight diameter of aggregates (MWD), organic carbon (OC), saturated hydraulic conductivity (Ks), saturated water content (θs) and bulk density (Db) in the three adjacent land uses i.e. forest, agriculture and range land located at Fandoghlou region of Ardabil. Totally, 100 soil samples were systematically (100 × 100 m grade) taken from 0-15 cm depth in spring 2013. At first, the accuracy of Kriging and inverse distance weighting (IDW) geostatisticaly methods in mapping of studied parameters was evaluated then the final map was presented. The values of nugget effect to sill ratio for clay, sand and silt were 0.5, 0.47 and 0.49, respectively so these parameters have an average spatial structure. The values of above mentioned ratio for OC, Db, θs, Ks, and MWD were obtained 0.002, 0.014, 0.0007, 0.05 and 0.008, respectively, indicating strong spatial structure. According to the R2 criteria, Kriging method in estimating clay, sand and silt and IDW method in estimating MWD, OC, Ks ،θs and Db had the highest accuracy. The final map indicated that forest land had higher OC, MWD and Ks and lower Db compared with agriculture and range land. The results of this research showed that soil physical quality of the studied region in agriculture and range land uses was lower than forest lands.
Ali reza Karimi; Isa Esfandiarpour Borujeni
Abstract
Soil maps are the common sources of soil information for land evaluation and land use planning. The objective of this study was to evaluate the capability of conventional and geostatistical methods for mapping selected physical (sand, silt and clay) and chemical (carbonate calcium equivalent and pH) ...
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Soil maps are the common sources of soil information for land evaluation and land use planning. The objective of this study was to evaluate the capability of conventional and geostatistical methods for mapping selected physical (sand, silt and clay) and chemical (carbonate calcium equivalent and pH) soil properties. Based on interpretation of aerial photographs, satellite images and field observations, five geopedologic map units were identified in an area of about 12 km2 in southern Jiroft. 100 surface soil samples (0-20 cm) were taken from a regular grid of 500 × 250 m. The results indicated that geopedological map units were significantly different in at least one soil property. Differences of characteristics between units are resulting differences in geomorphic processes. Continuous soil maps prepared by the ordinary kriging also revealed continuous variations of characteristics in accordance with the changes in geomorphic processes. However, variations between units obviously recognizable in the boundary of units were not revealed by the geostatistical method. Based on results of this study, the conventional method is proposed for large areas (small scale maps) and geostatisticals method for small areas (large scale maps) are proposed for soil mapping.
habib beigi
Abstract
Boroujen–Fradonbeh plain is one of the nine main agricultural hubs of Charmahal Provine. The aim of this study was to define and map a deficiency index of soil micronutrients and the effect of wastewater application on it. For this, 200 surface soil (0-30 cm) samples were randomly collected and plant ...
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Boroujen–Fradonbeh plain is one of the nine main agricultural hubs of Charmahal Provine. The aim of this study was to define and map a deficiency index of soil micronutrients and the effect of wastewater application on it. For this, 200 surface soil (0-30 cm) samples were randomly collected and plant available concentrations of copper, zinc, iron, and manganese were determined. After variography and determining the most suitable spatial estimation method, maps of each micronutrient was drawn, normalized, and ranked. An integrated deficiency map was then constructed using the weights from rank maps. According to the maps of copper, zinc and iron, the available concentrations increased from west to east of the plain. This increase was attributed to the wastewater irrigation. The mean value of the integrated map, namely 85.5, indicated the seroius soil deficiency of micronutrients in this plain where 34% of the area was showing severe deficiency. Wastewater application has increased the overall availability of micronutrients by 4%. Sensivity analysis indicated that the map was most sensitive to zinc. Therefore, zinc concentration must be monitored with more precision and frequency across the plain.
I. Esfandiarpour Borujeni
Abstract
Soil salinity and sodicity are considered as the important factors limiting the plants growth. This study was conducted to assess the influence of the sample size on the accuracy of estimation of soil salinity and sodicity status, made by ordinary kriging, inverse distance weighting and spline estimators ...
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Soil salinity and sodicity are considered as the important factors limiting the plants growth. This study was conducted to assess the influence of the sample size on the accuracy of estimation of soil salinity and sodicity status, made by ordinary kriging, inverse distance weighting and spline estimators in Eslamieh area, Rafsanjan city. First, electrical conductivity (EC) and sodium adsorption ratio (SAR) were measured for 100 observation points, collected from three depths using a regular grid sampling pattern with an interval of 500 meter. These properties were mapped using aforesaid estimators. Then, random omission of 20, 40 and 60 samples from the total primary dataset (100 samples), was done and in each new situation, EC and SAR were mapped again. At the end of all 10 stages used to omit the samples, the index of standardized root mean square error (RMSE%) was measured for each estimator. Finally, the obtained contents of RMSE% were statistically compared using Friedman and Wilcoxon tests. The results showed using relatively high number of samples (all 100 observation points), three analyzed estimators have no significant difference (95% confidence level). In the cases of lower sample sizes, Friedman test revealed a significant difference among the estimators; whereas using Wilcoxon test, as a supplementary procedure, no significant difference was observed between the results obtained from ordinary kriging and inverse distance weighting. Hence, thanks to the relatively good precision, ease of processing and lower required sampling points, the inverse distance weighting estimator is recommended for future studies in the studied area.
Kh. Ghorbani
Abstract
So far several methods have been developed for mapping and interpolation of isohyets.one of the recently accepted methods is geographically weighting regression which is suitable for evaluation of spatial heterogeneity of dependent variable by using local regressions. In order to evaluate annually precipitation ...
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So far several methods have been developed for mapping and interpolation of isohyets.one of the recently accepted methods is geographically weighting regression which is suitable for evaluation of spatial heterogeneity of dependent variable by using local regressions. In order to evaluate annually precipitation spatial variation, this study was conducted in Gilan province which precipitation is distributed non-uniform due to different environmental conditions. The results of geographically weighting regression method were compared with another interpolation methods including global polynomial, local polynomial, inverse distance weighting (IDW), spiline, kriging and co-kriging and . In this study, average of 20 years annually precipitation data of 185 meteorological observations over Gilan Province and its neighboring stations was used for modeling of spatial distribution variations of mean annual precipitation by using other variables like elevation and position of points to the sea level. Cross validation technique was used to assessment accuracy of each interpolation methods. The result showed that geographically weighting regression method had minimum error with RMSE=147 and had significant difference with the kriging method which was in the second rank with RMSE=187. Finally the best method for mapping isohyets in Gilan province is geographically weighting regression method.
