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The incidence of climate events such as droughts and floods in any given region is intricately tied to the temporal and spatial distribution of precipitation. In hydrological modeling, precise analyses of precipitation trends and extreme precipitation indices hold significant importance. This study aims to examine the trends in annual precipitation averages and extreme precipitation indices using a quantile regression (QR) model across 39 synoptic stations in Iran over a 50-year statistical period (1972-2021). Iran experienced its highest annual precipitation average in 1982, reaching 491.6 mm, while the lowest was recorded in 2021 at 218.3 mm. The quantile regression model analysis revealed a downward trend in Iran's annual precipitation averages across the 0.05, 0.5, and 0.95 quantiles, with significance levels of 0.1, 0.05, and 0.01, respectively. Extreme precipitation indices in the northern and western parts of Iran were notably higher than in other regions. The R10 and R20 indices also represent the number of days with at least 10 mm and 20 mm of precipitation, respectively. They show a decreasing trend in northern and northwestern Iran at significance levels of 0.1, 0.05, and 0.01. These trend analyses offer valuable insights into annual precipitation averages and extreme indices, aiding water resources.

Introduction: Soil penetration resistance (PR) and soil shear strength (SS) are used to evaluate soil erodibility. Some soil properties such sand, silt, clay, bulk and particle density, total porosity, organic carbon and CaCO3 and also some land characteristics such as percentage and direction of slope, altitude, type and density of vegetation can affect SS and PR. For example, PR values exceed 2.5 MPa, while root elongation is significantly restricted. Most of soil properties have temporal and spatial variabilities. Therefore, it is necessary to use geostatistical methods to simultaneously use quantitative information and geographic location of variables. The forest, range and cultivated soils of Fandoghloo region of Ardabil are located in sloping lands and are subject to erosion. Therefore, it is necessary to know the state of spatial variability of soil SS and PR as two important indicators affected by compaction and also effective on soil erosion in the mentioned area. The main objectives of this research were: 1) Investigating the spatial variabilities and drawing maps of soil SS and PR in forest, range and cultivated lands of Fandoghloo region of Ardabil, 2) Investigating the correlations between soil SS and PR with other soil characteristics in the study area, 3) Determining semivariogram parameters such as semivariogram models, spatial dependence classes and effective range for soil variables, 4) Comparison of the accuracy of geostatistical methods (ordinary kriging (OK) and inverse distance weighting (IDW)) in the interpolation of SS and PR. Methodology: This study was conducted in the forest, range and cultivated lands of Fandoghloo region of Ardabil located at the 25 km of Ardabil city, northwest of Iran (48° 32ʹ 45ʺ to 48° 33ʹ 5ʺ E and 38° 24ʹ 10ʺ to 38° 24ʹ 25ʺ N) at summer 2023. Totally, 80 geo-referenced samples were taken from 0-10 cm soil depth with 50×50 m intervals (15 ha) in cultivated (n=37), range (n=23) and forest (n=20) land uses. Sand, silt, clay, organic carbon (OC) and particle density (PD) were measured in the disturbed soil samples. Bulk density (BD) and field water content (FWC) were measured in the undisturbed soil samples taken by steal cylinders with 5 cm diameter and height. Total porosity was calculated using BD and PD. Soil penetration resistance (PR) was directly measured in the field at three replicates using a cone penetrometer. Soil shear strength (SS) was obtained using shear vane in saturation condition in the field at three replicates. The best fitted semivariograms model (Gaussian, spherical and exponential) was chosen by considering the minimum residual sum of square (RSS) and maximum determination coefficient (R²) for soil variables. Ordinary Kriging (OK) and inverse distance weighting (IDW) interpolation methods were used to analyze spatial variability of soil SS and PR. Spatial distribution maps of soil variables were provided by Arc GIS software. Normality test of data by Kolmogorov-Smirnov test and Pearson correlations were done using SPSS software. Figures were prepared using Excel software. The accuracy of OK and IDW methods in estimating soil SS and PR was evaluated by mean error (ME), mean absolute error (MAE), root mean square error (RMSE) and concordance correlation coefficient (CCC) criteria. The CCC indicates the degree to which pairs of the measured and estimated parameter value fall on the 45° line through the origin. Conclusions: Results showed that PR negatively related to the silt and FWC. Also, SS negatively related to the silt and positively related to the sand and OC. The strong spatial dependency was found for PR and medium spatial dependency was determined for SS in the studied area. The silt revealed the smallest effective range (636 m) among the studied variables. As a suggestion, for subsequent study, soil sampling distance could be taken as 636 m instead of 50 m in order to save time and minimize cost. [ABSTRACT FROM AUTHOR]

Determination of the physico-chemical characteristics of soil for sustainable agriculture on large scales is an important factor in achieving a precision agriculture. Different land use and management practices greatly impact soil properties, and knowledge of the variation in soil properties within different land uses, is essential in determining production constraints related to soil characteristics. Laboratory analyses of the soil properties are usually expensive and time consuming. Surmounting these problems is possible using geostatistics. This study was conducted to assess geostatistical methods for the spatial distribution of some soil properties of Darengan region with different land uses in Fars province. 134 surface soil samples at an interval of 1.0 × 1.0 km on a grid design were taken from pasture and agricultural land uses. Physico-chemical characteristics of the soil samples were analyzed. According to the results, the best spatial structure model with the highest accuracy was exponential model for the variables of sand, EC, CCE, pH, and BD, rational quadratic model for silt, and spherical model for clay, OC, and CEC. The spatial structure was weak for CCE, medium for organic carbon, and strong for the other variables. Among the characteristics studied, the variables of silt, clay and cation exchange capacity have the lowest range, and EC has the highest range. Based on the zoning map of the studied properties, the areas with agricultural land use had greater OC, clay, CEC, EC and lower pH. Understanding soil properties with their spatial dependency is of crucial importance for understanding the behavior of soil and hence providing better soil management. [ABSTRACT FROM AUTHOR]

Heavy metals are known to be the most dangerous environmental pollutants because they do not decomposed 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. Therefore, in order to control the quality of the environment, it is important to study the heavy elements in the soil. So far, many researchs have been done to identify contaminated areas and implement sustainable land use policies regarding 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. Geostatistical tools (ordinary kriging) were used. Sampling was performed at 209 points on a grid with distances of about 1.5 x 1.5 km in agricultural and urban land uses and about 2x2 km in uncultivated lands. The concentration of total nickel and absorbable soil was measured by atomic absorption spectrometer. Statistical and geostatistical measures were performed by SPSS, Variowin and WINGSLIB software and contamination map was drawn with Surfer 16 software. The results indicated that the concentration of nickel in various forms in the region does 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. [ABSTRACT FROM AUTHOR]

Knowledge about the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. During the last two decades, the utilization of data mining approaches in spatial modeling of SOC using machine learning algorithms have been widely taken into consideration. The essential step in applying these methods is to determine the environmental predictors of SOC optimally. This research was carried out for modeling and digital mapping of surface SOC aided by soil properties ie., silt, clay, sand, calcium carbonate equivalent percentage, mean weight diameter (MWD) of aggregate, and pH by machine learning methods. In order to evaluate the accuracy of random forest (RF), cubist, partial least squares regression, multivariate linear regression, and ordinary kriging models for predicting surface SOC in 141 selected samples from 0-30 cm in 680 hectares of agricultural land in Khorramabad plain. The sensitivity analysis showed that silt (%), calcium carbonate equivalent, and MWD are the most important driving factors on spatial variability of SOC, respectively. Also, the comparison of different SOC prediction models, demonstrated that the RF model with a coefficient of determination (R2) and root mean square error (RMSE) of 0.75 and 0.25%, respectively, had the best performance rather than other models in the study area. Generally, nonlinear models rather than linear ones showed higher accuracy in modeling the spatial variability of SOC.

Quantitative description of the spatial variability of soil hydraulic characteristics is crucial for planning, management and the optimum application. Field measurement of infiltration is very expensive, time-consuming and laborious. Soil structure also important effects on water infiltration in the soil. The objectives of this study were to determine the spatial variability of water infiltration, to select the most appropriate infiltration model, to calculate the parameters of relevant models, and to quantify the soil structure by using the fractal geometry. Infiltration parameters were estimated by using some physical soil properties, as well as fractal parameters, in this research. To achieve these purposes, 161 sites were selected and their infiltration was measured by using the constant head double-ring infiltrometers method in a systematic array of 500*500 m. The observed infiltration data from all examined sites were fitted to three selected infiltration models. Soil bulk density (BD), soil water content, soil particle size distribution, soil aggregate size distribution (ASD), organic carbon content (OC), saturation percentage (SP), soil pH and electrical conductivity (EC) were also measured in all 161 sites. For the quantitative assessment of soil structure, the aggregate size distribution, fractal parameters of the Rieu and Sposito model as well as the mean weight diameters (MWD) and geometric mean diameter (GMD) were also obtained. The obtained results indicated that the infiltration rates of the studied areas had generally low basic infiltration rates (1.1-31.1 cm hr-1) for most sites with the average of 6.69 cm hr-1. According to all obtained results and based on the least-square method, the Philip model was selected as the best performing model to account for infiltration. The aggregate size distribution demonstrated a fractal behavior, and the infiltration parameters could be significantly correlated with the fractal parameters and other soil physical properties.

Knowledge about the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. During the last two decades, the utilization of data mining approaches in spatial modeling of SOC using machine learning algorithms have been widely taken into consideration. The essential step in applying these methods is to determine the environmental predictors of SOC optimally. This research was carried out for modeling and digital mapping of surface SOC aided by soil properties ie., silt, clay, sand, calcium carbonate equivalent percentage, mean weight diameter (MWD) of aggregate, and pH by machine learning methods. In order to evaluate the accuracy of random forest (RF), cubist, partial least squares regression, multivariate linear regression, and ordinary kriging models for predicting surface SOC in 141 selected samples from 0-30 cm in 680 hectares of agricultural land in Khorramabad plain. The sensitivity analysis showed that silt (%), calcium carbonate equivalent, and MWD are the most important driving factors on spatial variability of SOC, respectively. Also, the comparison of different SOC prediction models, demonstrated that the RF model with a coefficient of determination (R2) and root mean square error (RMSE) of 0.75 and 0.25%, respectively, had the best performance rather than other models in the study area. Generally, nonlinear models rather than linear ones showed higher accuracy in modeling the spatial variability of SOC. [ABSTRACT FROM AUTHOR]

Introduction: Soils form from the interplay of five main factors namely parent material, time, climate, relief (topography) and organisms. Topography is one of the local factors that has direct and indirect effects on soil formation, physical and chemical properties of soils. To understand the mutual relationship between topographic properties, soil properties and plant community (phytocoenosis), it is necessary to decide on the appropriate method for properly managing the soil resources. In addition to the soil properties, topography may affect the soil production indices as well. Soil production index and consequently its productivity will in turn affect the growth and fruiting. Insight about the pattern the spatial variability of soil properties can be used to manage the lands properly. This study was performed to investigate the spatial variability of soil properties regarding aspect and also the relationship of these changes with the quality and quantity of peach production in Saman region in Chaharmahal-Va-Bakhtiari province, Iran. Materials and Methods: The study area contained 1.5 hectare of 200-hectare peach gerdens belong to BaghGostaran Company located in Saman, Chaharmahal-Va-Bakhtiari Province. The soil moisture and temperature regimes are xeric and mesic, respectively. 136 soil samples were collected from 0-30 and 30-60 cm depths. Two peach trees around the soil samples were also selected. Then, soil physical and chemical properties including soil texture, percentage of calcium carbonate equivalent, organic carbon, plant available potassium, phosphorous, iron and zinc, pH and electrical conductivity were determined and fruit properties including branch length and diameter in the current year, number of fruits, total yield, average of fruit weight, TSS, tissue strength, pH, acid and extract percentage were measured. Finally, the dataset were analyzed using Statistica 6.0 software. Analysis of spatial data was calculated via variogram and performed using Variowin, 2.2 software package. After determination of the best model, kriging maps of the soil and fruit properties were prepared by Surfer 8 software. Results and Discussion: The statistical results revealed that among the soil properties, pH of the surface and subsurface horizons in both aspects had the lowest CV. Plant available phosphorous and iron showed the highest CV at surface and subsurface horizons of eastern aspect, respectively. Among the soil variables, plant available iron showed the highest CV for both horizons at western aspect. Regarding peach properties, the tissue strength showed the highest and pH showed the lowest variation in both slopes, respectively. The results of mean comparisons revealed that the soil of eastern slope has more clay percentage, silt, organic carbon, plant available potassium, phosphorous, and iron in comparison with western aspect. Peach yield was higher in eastern aspect than the western one. Correlation coefficient among soil and peach propertied did not show a similar trend for two aspects. Amount of clay and plant available potassium in subsurface horizon showed a positive significant correlation with yield in western and eastern aspects, respectivelty. Variography showed that all variables except pH of subsurface horizon have spatial structure. The pattern of spatial variability of the yield and the number of fruits was also approximately the same as that of clay particles and organic carbon, plant available potassium, phosphorous and iron in both depths. The spatial variability of the branch length and diameter in the current year was similar to the spatial variability of clay particles percentage and the plant available potassium. The results suggested the significant effect of soil properties, especially clay particles percentage and the plant available potassium on the performance and vegetative properties of peach. However, the peach qualitative properties showed no significant correlation with the soil properties. Conclusion: The results suggest that the significant effect of aspect on the soil and fruit properties. It seems that the aspect caused the formation of soil with different properties. Significant differences observed among some soil properties including texture components, the amount of organic carbon and nutrients in both aspects. The trees on the eastern slope had higher yield due to having more organic carbon and nutrients and consequently higher soil quality, while the trees on the western slope had fruits with higher quality which may be due to the climatic factors such as receiving more light, or other soil properties like the mount of available nitrogen. More investigation is needed to understand the effect of NPK and iron fertilizers and climate properties on peach properties in the orchards of the area. The effect of climatic factors on the peach qualitative and quantitative characteristics should be investigated as well.

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.

Quantitative description of the spatial variability of soil hydraulic characteristics is crucial for planning, management and the optimum application. Field measurement of infiltration is very expensive, time-consuming and laborious. Soil structure also important effects on water infiltration in the soil. The objectives of this study were to determine the spatial variability of water infiltration, to select the most appropriate infiltration model, to calculate the parameters of relevant models, and to quantify the soil structure by using the fractal geometry. Infiltration parameters were estimated by using some physical soil properties, as well as fractal parameters, in this research. To achieve these purposes, 161 sites were selected and their infiltration was measured by using the constant head double-ring infiltrometers method in a systematic array of 500*500 m. The observed infiltration data from all examined sites were fitted to three selected infiltration models. Soil bulk density (BD), soil water content, soil particle size distribution, soil aggregate size distribution (ASD), organic carbon content (OC), saturation percentage (SP), soil pH and electrical conductivity (EC) were also measured in all 161 sites. For the quantitative assessment of soil structure, the aggregate size distribution, fractal parameters of the Rieu and Sposito model as well as the mean weight diameters (MWD) and geometric mean diameter (GMD) were also obtained. The obtained results indicated that the infiltration rates of the studied areas had generally low basic infiltration rates (1.1-31.1 cm hr-1) for most sites with the average of 6.69 cm hr-1. According to all obtained results and based on the least-square method, the Philip model was selected as the best performing model to account for infiltration. The aggregate size distribution demonstrated a fractal behavior, and the infiltration parameters could be significantly correlated with the fractal parameters and other soil physical properties. [ABSTRACT FROM AUTHOR]

The paddy soils in Lenjan area exposed to pollution owing to uncontrolled discharge of sewage sludge, wastewater and unessential fertilizers. Little information exists on Cadmium (Cd) distribution in paddy soils of Isfahan Province, this study was therefore investigated the spatial variability of cadmium which is considered as the most toxic metals. 90 soil samples (0-20 cm) were collected from study area. Soil properties such as pH, EC, calcium carbonate equivalent, soil texture, organic matter and cation exchange capacity were measured. The total and available Cd concentrations of soil samples analyzed by atomic absorption spectrophotometer. In addition, estimation of spatial data performed via kriging interpolation method (ordinary and blocky kriging) and by GIS. The total and available concentration of Cd in the study area were averagely 1.747 and 0.073 mgkg-1 respectively, which were much higher than the standard limit and classified in high pollution. Geostatistical analysis result was shown that exponential and spherical models for the total and available Cd concentration were best model, respectively. The most effective range of total and available Cd was 1011 and 1050 meter respectively and correlation ratio was weak in this range. Evaluation of correlation coefficient, MEE and RMSE parameters showed that both methods of kriging for data estimation in comparison with real data had acted in an appropriate manner. The result also showed that human activities such as industrial and urban wastewater entering to the water resources and application of excessive fertilizers had an impact on cadmium concentrations significantly.

The aim of this work was to study distribution of some heavy metals in different soil particle-size fractions and to assess their spatial distribution. The study was carried out in Kafe Moor (Kerman, Iran) where the Gol-Gohar Iron Mine is located. One hundred twenty composite soil samples were randomly collected and transferred to the laboratory in bags. After air-drying, the samples were fractionated into six classes including 2- 0.5, 0.5-0.25, 0.25-0.125, 0.125- 0.075, 0.075-0.05 and <0.05 mm. Elemental concentrations (Fe, Mn, Cu, Zn, Pb and Ni) were determined using acid digestion method (HNO3, 4.0 N) and an atomic absorption spectrophotometer in each class. Ordinary Kriging technique was used for predicting spatial distribution of heavy metals. The results showed that content of metals in soil increased with decreasing particle size. The results also showed that the concentration of Fe, Mn, Cu, Zn, Pb and Ni in <0.05 mm size fraction were 2.13, 1.70, 4.79,2.43, 1.42, and 3.47 times higher than in 2-0.05 mm size fraction, respectively. In addition, mapping the concentrations of heavy metals with kiriging showed that metals pollution decreased with increasing distance from mines area [ABSTRACT FROM AUTHOR]

Salinity and alkalinity decreases physical, chemical and biological quality of soils and as a result reduces crop yield. This study aims to evaluate spatial variability of soil salinity in Ahvaz using geostatistical approaches. Accordingly, 69 surface soil samples (0-10 cm) were collected and their electrical conductivities (EC) were measured in 1:1 soil: water extracts. The data were then analyzed using ordinary kriging (OK), log-normal kriging (LOK) and indicator kriging (IK) interpolation techniques to produce soil salinity maps. Finally, the quality control of soil maps was performed by calculation of root mean square error (RMSE) and coefficient of determination (R2). The results indicated that due to the lowest RMSE and the highest R2 values, the LOK interpolation method is the best approach in mapping soil salinity in Ahvaz. The results also illustrated that based on defined threshold values (4, 8, 16, and 32 dS m-1) the indicator kriging methods have been able to show risk of soil salinity in the area. Based on this, most of the area is covered by soils with salinity higher than 4 dS m-1. Evaluation of final soil maps showed that the highest concentrations of salts are related to the western and southwestern parts of Ahvaz city. In contrast, the lowest amounts of salinity were found in Eastern and Northern parts of the city. [ABSTRACT FROM AUTHOR]