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Precipitation is a key constituent of the water cycle and its accurate measurement is essential for a wide range of hydroclimatic studies. Although advances in technology have provided new sources of precipitation data (e.g., satellite data), gauge measurements are still considered as the most reliable source of information. Accuracy of ground measurements highly depends on the network density and positions of the rain gauges and thus, many studies have attempted to re-design the rain gauge networks around the globe. This is while, a limited number of researches have investigated the potential of utilizing global gridded precipitation products in network optimization context. In this study, a two-step framework was proposed to remove redundant rain gauge stations of the Central Plateau watershed of Iran. In the first step, number of excess rain gauges was detected based on transinformation–distance (T–D) analysis. In the second step, exact location of the extra rain gauges was determined through high degree of freedom and low degree of freedom (LDF) scenarios by a combination of harmony search optimization algorithm and a geostatistical model. According to the results, 58 out of 580 rain gauges were detected to be removed. Results of the proposed scenarios differed in detecting 13 out of 58 gauges. While LDF scenario required less computational time, network re-designed by this scenario was about 32% less accurate than the other scenario. Performance of the both optimized networks was equally similar to the initial network in terms of developing precipitation rasters. [ABSTRACT FROM AUTHOR]

The mapping of cation exchange capacity (CEC) was assessed using the geostatistical technique based on principal component analysis (PCA) in the Khuzestan province, southwestern Iran, for the proper management of agricultural lands. To this end, 210 topsoil samples were obtained from the area at the depth of 0–30 cm by systematic sampling in regular networks with dimensions of 350 × 350 m. Soil attributes were fully analyzed using both classical and geostatistical methods, and the prediction map of soil CEC was created by ordinary kriging (OK) and cokriging (CK) methods. The findings revealed that soil properties dominating the first principal component (PC1) explained the high correlation observed between soil properties such as CEC (r2 = 0.89). Still, a weak or no correlation between other components and CEC was obtained in the correlation matrix. According to the PCA results, the PC1 was used as an auxiliary variable to estimate the soil CEC in the CK method. The precision of the prepared maps was determined by the cross-validation analysis using NRMSE, MAE, and R2. Based on the cross-validation result of the predicted dataset, the NRMSE and MAE for OK were 0.25, 2.9, and 0.34 cmol(+) kg–1, and 0.18 and 2.1 cmol(+) kg–1 for CK, respectively. The cross-validation R2 for the prediction dataset was 0.31 for OK and 0.84 for CK methods at the 0.01 level, respectively. The result also revealed that the CK interpolation method with PC1 could effectively predict soil CEC variation. Furthermore, the combination of the CK method and PC1 derived from soil attributes was reasonably able to predict soil CEC. Principal components, which have a good correlation with the dependent variables, the best potential for CK predictions, and the application of CK interpolation method with PC1 can reduce the time and cost for CEC mapping. [ABSTRACT FROM AUTHOR]

Soil fertility and plant nutrition management have received much attention in order to increase efficiency, improve plant productivity and also reduce environmental hazards. Soil cation exchange capacity (CEC) is a critical indicator of soil fertility quality and pollutant separation capacity. Plants such as rice need to provide their nutrients using fertilisers for maximum production, so it is essential to know the content of the major elements such as nitrogen, phosphorus and potassium in the soil and to prepare their ideal maps. To evaluate the factors affecting soil fertility and preparation of their maps, a total of 255 soil samples were collected from the study area located in northern Iran. The soil CEC, total nitrogen, available potassium and phosphorus were analysed using standard methods. Spatial variability maps of soil fertility properties were prepared using Ordinary Kriging method. The results obtained from field data show that the amounts of total nitrogen, available potassium and phosphorus are more than optimal limit in many parts of the study area. Evaluation criteria values including normalised root mean square error (NRMSE), normalised mean absolute error (NMAE) and coefficient of determination (R2) were derived for CEC 0.164, 0.135 and 77.65, total nitrogen 0.232, 0.298 and 71.59, available phosphorus 0.214, 0.215 and 70.11 and available potassium 0.197, 0.146 and 78.15, respectively, in the test data group. NRMSE, NMAE and R2 values showed that the accuracy of prepared maps was ideal. The spatial distribution maps can be used as an appropriate guide for precise and specific management of NPK nutrients application to protect the environment and stop the contamination of groundwater resources within the study area. [ABSTRACT FROM AUTHOR]

Modeling agriculture land suitability at a regional scale plays an important role in designing the best sustainable management systems. The aim of this study was to derive a land suitability map for wheat farming by combining the Geostatistics and analytic hierarchy (AHP)-Fuzzy algorithm in geographic information system (GIS) in calcareous and saline–sodic soils, southern Iran. The local expert's opinions were used to make a decision on the weighting of climate, terrain, and soil data by applying an AHP method. The input data were transformed to a fuzzy-set data. The Spherical and Gaussian semi-variogram models had the best performance for fitting the soil parameters. The results revealed that soil texture (w = 0.207), pH (w = 0.121), slope (w = 0.120), electrical conductivity (w = 0.113), and exchangeable sodium percentage (w = 0.111) had the highest specific weighting for wheat production, respectively. The land suitability map indicated that 25.65% (48306.6 ha) of the studied area was for highly suitable, 38.2% (71939.7 ha) was moderately suitable, and 27.63% (52017.2 ha) was marginally suitable. Only 8.52% (16042.4 ha) of the studied area was not suitable for wheat farming. In conclusion, a combination of AHP, Fuzzy, and GIS could be a potential approach for site-specific soil management, land-use planning, and protection of the environment. [ABSTRACT FROM AUTHOR]

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

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

[Display omitted] • Ordinary kriging (OK) and support vector machine (SVM) were used to map pedo-geomophometric covariates. • Models used to map pedo-morphometric covariates had similar performance metrics. • OK and SVM maps of pedo-geomorphometric covariates were used to develop management zones (MZs) • SVM-devived MZs predicted soil organic carbon with higher accuracy compared to OK-derived MZs. • Developed MZs can support cleaner sustainable agricultural practices and improve food security in Iran. Site-specific management of soil organic carbon (SOC) is crucial for cleaner, sustainable agricultural production and climate change mitigation. The Neyshabur plain of northeastern Iran is plagued with spatially variable critically low SOC. Thus, in this study management zones (MZs) were developed for the site-specific management of SOC. Soil samples (0–0.3 m) and digital elevation model-derived geomorphometric variables were collected at 288 locations. Soil pH, clay content, available phosphorus, available potassium, and the vertical distance to channel network significantly (p < 0.05) correlated with SOC. Subsequently, support vector machine (SVM) and ordinary kriging (OK) approaches were used to map the spatial variability and develop MZs based on SOC and its above-listed pedo-geomorphometric covariates. Generally, the coefficient of determination (R2) and root mean square error (RMSE) of OK and SVM models used to interpolate pedo-geomorphometric covariates, were similar. However, the Fuzzy Performance Index (FPI) and Normalized Classification Entropy (NCE) indices suggest that the MZs based on the SVM model (FPI = 0.091, NCE = 0.113) are more accurate compared to those determined using the OK (FPI = 0.135, NCE = 0.164) method. Although the results demonstrate the potential of both OK and SVM approaches for site-specific management of SOC in the Neyshabur plain, the average difference of 0.8 g kg−1 SOC in the SVM approach compared to OK (p < 0.05) was found to be significant, especially along transitional boundaries between MZs. Thus, the SVM-based MZs developed in this study can be used for more accurate site-specific application of organic amendments, mineral fertilizers, and management practices that improve carbon sequestration and sorption of chemical contaminants in the Neyshabur plain of Iran. [ABSTRACT FROM AUTHOR]

The suitability of groundwater for paddy field irrigation in the alluvial plains of Guilan Province, northern Iran, was investigated using ordinary kriging and ordinary cokriging of continuous and indicator quality variables. The cross validation values of error measures showed that ordinary cokriging provides more accurate estimates of the quality variables of interest. Maps showing the spatial variability of electrical conductivity (EC) and sum of major cations and anions (SCA) were generated for the years 2010 through 2014, using ordinary cokriging. Based on the estimated values of EC and SCA, four groundwater salinity classes (excellent, good, risky, and unsuitable) were considered and the proportion of the study area covered by each class was obtained. Results showed that the portion of the study area covered by the risky class, in which the groundwater salinity is expected to reduce the rice yield, is located in the eastern part of the study area and has an average value of 25.4% in the period 2010–2014. The results also showed that the western part of the study area has excellent or good groundwater quality for rice irrigation. The probability maps of EC were also obtained using ordinary cokriging of EC indicator variable. Five probability classes were considered and the proportion of the study area covered by each class was obtained. It was observed that the probability that the rice yield is reduced more than 10% is above 0.4 in 6.2% of the study area. The maps generated in this study can be used to identify the regions in the province where groundwater could be allowed to be extracted and utilized by farmers to reduce the bad effects of the scarcity of surface water. Also, in the regions with a risk of rice yield reduction, conjunctive use of groundwater and surface water could be planned and advised to farmers. [ABSTRACT FROM AUTHOR]

The aims of this study were: 1) to map the different soil parameters using three geostatistical approaches, including; ordinary kriging (OK), cokriging (CK), and regression kriging (RK), 2) to compare the accuracy of maps created by the mentioned methods, and 3) to evaluate the efficiency of using ancillary data such as satellite images, elevation, precipitation, and slope to improve the accuracy of estimations. In the rangelands of the Poushtkouh area of central Iran, 112 soil samples were collected. The maps of different soil parameters were created using the mentioned methods. To assess the accuracy of these maps, cross-validation analyses were conducted. The cross-validation results were assessed by the root mean square error (RMSE) and normal QQ-plot together with sum and average error to suggest the best estimation approach for mapping each soil parameter. The results have shown that, in most cases, taking the ancillary data into account in estimations has increased the accuracy of the created maps. Except for clay, the OK method was suggested as the best estimation method, and the RK and CK were the best recommended estimation methods for the rest of the parameters. The results suggest the application of the framework of this study for similar areas. [ABSTRACT FROM AUTHOR]

The distribution variability of soil electrical conductivity (EC), pH, clay, sand, CaCO3, organic carbon (OC) and available potassium (K) in the Naqade region was investigated using a geostatistical method and Geographical Information System (GIS) technique. Two hundred and eighty-two topsoil (0–30 cm) samples were randomly collected and analyzed. pH and clay followed a normal distribution, whereas sand EC, CaCO3, OC and K were log-transformed. The highest variation was observed for soil EC, and the lowest for soil pH. In the variography analysis, spherical, exponential and gaussian models were best fit on experimental semivariograms. The minimum effective spatial autocorrelation was 1500 m for OC and the maximum effective spatial autocorrelation was 4000 m for sand and K. Strong spatial correlations were noted with sand and CaCO3(<25%), whereas values were moderate for clay, EC, OC and K (25–75%). Ordinary kriging was utilized for the interpolation of estimated variable determinations in unsampled sites. It was found that soil properties in this study area were strongly influenced by both environmental and natural factors. The results can be used as a source of information for the development and implementation of any further land management and soil and water conservation plans. [ABSTRACT FROM AUTHOR]

Abstract: This study addressed the effect of aspect on spatial variability of some of soil physicochemical properties in Central Iran. Two north-facing and south-facing slopes were selected and 102 topsoil samples were randomly collected. Results showed that the spatial distribution and spatial dependency level of the same soil attributes were different in two aspects. Regarding to the kriged maps, only trends of soil pH and silt percentage were more or less similar for both aspects. Our findings suggest that local-scale soil variability in different aspects could grow unstably over time and bring about an unstable and chaotic condition for soil pedogenesis. [Copyright &y& Elsevier]

Analysis of the spatial variability of soil properties is important to explain the site-specific ecosystems. Spatial patterns of some soil properties such as soil texture, exchangeable sodium percentage (ESP), electrical conductivity (ECe), soil pH and cation exchange capacity (CEC) were analyzed in salt and sodic affected soils in the south of the Ardabil province, in the northwest of Iran, to identify their spatial distribution for performance of a site-specific management. Soil samples were collected from 0 to 30, 30 to 60, 60 to 90, 90 to 120 and 120 to 150 cm soil depths at sampling sites. Data were investigated both statistically and geostatistically on the basis of the semivariogram. The spatial distribution model and spatial dependence level varied in the study area. Among the considered parameters, maximum and minimum spatial variability were observed in EC and pH parameters, respectively. Soil properties showed moderate to strong spatial dependence, except for a few. ECe was strongly spatially dependent in the total soil depth and clay was strongly spatially dependent at the first depth. Sand and pH were moderately spatially dependent for three of the five depths. ESP was strongly spatially dependent and silt was moderate in the total soil depths, except at 90-120 cm depth. Furthermore, CEC had strong spatial dependence for three of the five depths. All geostatistical range values were >1,389 m in this study. It was concluded that the strong spatial dependency of soil properties would lead to extrinsic factors such as bedrock, agricultural pollution, drainage and ground water level. [ABSTRACT FROM AUTHOR]

Rain gauges in many watersheds are not enough to represent the spatial and temporal variations of precipitation. Many agricultural activities can be highly sensitive to these variations and therefore different monthly or annual precipitation statistics have been widely used in the literature for agricultural planning and management. For example, mean monthly rainfall has been used as one of the main criteria for classifying suitable regions for dryland farming. In this paper, a method for space-time estimation of cumulative mean monthly rainfall (CMMR) is presented. The proposed method for estimating CMMR pattern is based on developing time-varying variogram coupled with ordinary Kriging (OK). OK is selected as a spatial exploration method and all of its parameters are optimised using weighted least square method. K-means clustering has also been applied to increase the model efficiency. Space-time estimation of CMMRs in each cluster has been carried out based on the observed CMMRs of the rain gauges in each specific cluster. The proposed method is tested using 25 years of monthly rainfall records of 14 rain gauges in the Maroon watershed in south of Iran. The results of the case study have shown that both methods (estimation with and without clustering) are proper choices for space-time modelling of rainfall pattern; however, the method in which the rain gauges are classified to different clusters has shown a better statistical performance in terms of normalised mean square error. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Prediction of groundwater depth and elevation is important in quantitative water management especially in arid areas. There are several basins in southwest of Iran, in Zagross Mountain, in which the water wells are distributed along a narrow elliptic ring band around the region. To find the most applicable interpolation method, both of the groundwater depth and elevation are predicted by different kriging methods. It is found that the groundwater elevation and depth can be predicted by different methods. Furthermore, it is found that the methods in which the trend is eliminated predicted the groundwater elevation and depth in central part of the region is with less standard error. Furthermore, the methods with no trend elimination, predicted the groundwater depths with less error near the water wells. Dividing the area to hydro-geologically homogeneous sub-areas improved the interpolation precision. [ABSTRACT FROM AUTHOR]

Mud window is an inseparable part of well design. It is so crucial for having a successful drilling operation, especially in gas fields. Reliable and accurate design of mud weight is the key to having a safe wellbore. Many wellbore stability problems occur because of the inaccurate mud weight in both gas and oil reservoirs. Inaccurate and even sometimes wrong mud weight is caused by lack of adequate knowledge about the field geomechanics. Quantifying the geomechanical uncertainties allows taking the knowledge incompleteness into account. It can reduce the risks of unreliable mud weight design. The geomechanical parameters are only known at some sparse points of measurements. Therefore, it is necessary to estimate the parameters in the whole reservoir as if there is a decision to drill an infill well or conduct a well work-over or side-track an old well. Geostatistical algorithms are utilized to create 3D mechanical earth modeling (MEM) of the system. Several MEM realizations are created to span the range of geomechanical uncertainty adequately. The mud window is designed under uncertainty based on Mohr-Coulomb failure criteria. In this article, at first, 1D MEMs at well locations are built by the available data, and then ordinary Kriging is used to propagate 1D MEMs onto 3D MEM. Smooth plus rough – nonlinear problem (LMAP) and Rough plus smooth – nonlinear problem (RML approximate) are the methodologies applied to quantify the geomechanical uncertainties. In the end, the optimum mud window and the corresponding uncertainty ranges are identified based on the posterior probability distribution of geomechanical parameters in one of the gas reservoirs in the south of Iran. The resulting mud window is narrower compared to the mud window designed with prior information. Consequently, mud window design would be more reliable with less possibility of kick or any wellbore instability. • A novel methodology is developed to design a reliable safe mud window based on updated geometrical uncertainty distribution • The used approach allows to estimate the uncertainty ranges for geomechanical parameters and their dependent parameters. • 1D and 3D Mechanical Earth Modeling were built to obtain geomechanical parameters, using Ordinary Kriging methods [ABSTRACT FROM AUTHOR]

Irrigation water salinity is one of the factors that reduces agricultural production. Guilan Province is one of the most important rice-producing areas in Iran where groundwater is used for irrigation. The temporal and spatial variations of groundwater salinity were studied in the coastal strip covering 4285 km2 of the province using data from 73 wells, as well as its estimated effect on the rice yield. Data on mean electrical conductivity (EC) for each 6-month period of 12 consecutive years, from the second half of 2002 until the end of 2014, were analyzed and resulted in 25 mean ECs. EC maps and maps of the probability of higher salinity areas were obtained by using ordinary kriging (OK) and indicator kriging (IK) in ArcGIS 9.3 software, respectively. Thereby, areas belonging to different salinity classes were outlined and places with higher salinity reducing the rice yield were identified. In addition, the Mann–Kendall test and Sen's slope were used to project future changes. The results indicated that due to the salinity of groundwater in the coastal strip area, the minimum and the maximum rice yields were 80% and 100%, respectively. Using the IK method, higher probability of groundwater salinity reducing the yield was found from the central parts toward the east. The Mann–Kendal test result showed significant temporal trends of the size of areas below the 100% yield (EC < 1 dS/m) and 90–100% yield (1 < EC < 1.34 dS/m) thresholds. The equations given by Sen's slope estimator indicated that the groundwater salinity will not be a limiting factor for achieving 100% rice yields from the year of 2021 onward in all of the Guilan coastal area. The trend of increasing precipitation in the area may be an important cause. [ABSTRACT FROM AUTHOR]