زمین آمار

The aim of the present study is to analyze the groundwater quality of the Fariman-Torbat Jam plain in Khorasan Razavi for environmental protection purposes, which was carried out through field survey techniques, random sampling, and testing. During 2019-2020, 10 wells were sampled and water quality was determined with the index of IRWQIGC, and inverse distance and simple kriging methods were used to investigate spatial changes. EC was measured with a conductivity meter, TDS with a filtration method, hardness with a titration method, and nitrate with a UV-Vis spectrophotometer. The best water quality was in the fall (relatively good, index: 60.45) and the worst water quality was in the spring (relatively bad, index: 34.14). Also, the best groundwater quality was related to the Robat Samangan well (relatively good, index: 54.80) and the worst quality was related to the Qale Khaki well (bad, index: 28.75). 60% of the wells were in the saline category and 40% in the very saline category. There was no significant difference between the definitive method and the geostatistical method in terms of the RMS and MAPE. The groundwater in the south and southeast of the plain showed higher concentrations in terms of salinity and hardness, and the groundwater in the north and northwest showed higher concentrations in terms of nitrate than other places. In order to improve the quality of groundwater, it is necessary to minimize excessive exploitation and use of chemical fertilizers in a controlled manner.

The main activity of people in the Sistan region is agriculture and animal husbandry, which due to the drought and the unavailability of surface water and the intermittent flow of water in the Sistan River, the digging of irrigation wells has expanded in this area. The Sistan River continues of Afghanistan's Helmand River, which passes through agricultural and urban areas. Therefore, it is possible that some kinds of pollutants, especially heavy metals, can come from different sources. On the other hand, after entering the Sistan Plain, the river affects the water level of wells along the river. Therefore, in case of contamination, it may increase the concentration of heavy metals in wells. However, until now, the concentration of heavy metals in the water wells has not been investigated and only the salinity factor has been considered from the point of view of water quality. The quality of drinking water is associated with the concentration of physicochemical compounds such as nitrate, phosphate, various anions and cations, and organic and inorganic pollutants such as heavy metals. Drinking water contaminated with metals is turning into a primary health concern for human health care. Therefore, surveying the quality of water can be helpful in management. The aim of this study is to investigate the concentration of heavy metals in the wells along the Sistan River, in order to ensure the quality of water for agriculture, husbandry, and human consumption.

In this research, in order to ensure the appropriate quality of water in terms of the concentration of heavy metals and also the possible effect of EC and pH factors on their changes, the concentration of some elements and factors were mentioned in the water samples of 26 active wells. The samples were collected along the Sistan River (with a maximum distance of 1000 meters buffer zone) from the border point to the entrance to Hamoun Hirmand (Afzal-Abad branch, Lorg Bagh and Khwaje mountain) from January to March 2022. According to the investigation of the Afzal-Abad branch and the absence of wells, no samples were obtained from this section. At the same time as the sampling, the characteristics of the well, such as the year of establishment and the type of water usage were recorded. Sampling was done in three repetitions and during sampling pH, EC, TDS, and salinity variables were measured by a portable calibrated device and recorded three times. The collected samples were transferred to the laboratory in order to measure heavy metals in sterilized frosted glass containers. The standard method of the American Public Health Association (APHA) was used to measure each of the considered factors. After the preparation of samples, the concentration of heavy metals was measured with ICP. The obtained results were zoned using the normal kriging and co-kriging methods based on the selected model resulting from the prediction standard error. Geostatistical kriging methods (such as simple kriging, normal kriging, and co-kriging) were used for interpolation of heavy metals distribution. In the semi-variable analysis, the variability of the factors with respect to the spatial distance was organized by different linear, spherical, etc. functions using ArcGIS software.

The average amount of pH was measured at 8.31. The concentrations of salinity and TDS were 3.74 and 4.62 g l-1, respectively. EC value was also measured as 6322 µS/cm. The average concentration of Cr, Fe, Ni, Cu, Zn, and Pb was also obtained at 2.64, 124, 10, 5.9, 33, 17, 1.43, and 2.79 µg l-1 respectively. The trend of elements obtained Fe>Zn>Ni>Cu>Pb>Cr>Cd. The results indicated a low concentration of Cd, Ni, Cr, Cu, Fe, and Zn While Pb concentration was higher than the standard. The low concentration of the mentioned elements is due to the alkaline pH of water, which acts as a buffer and causes the elements to become insoluble and precipitate. Some dangerous elements such as mercury could not be measured due to their low concentration. The amount of salinity and EC factors also showed that the well water is not suitable for agriculture and livestock. The result of element zoning also showed that the concentration of metals increases from the border towards the lake. According to the age of the wells, it can be said that the reason for the decrease in the concentration of metals is due to the longer life of the wells and the chance of water mixing during the Sistan River water harvesting.

The result showed that the concentration of metals, except lead, is lower than the standard value. The low concentration of elements may be due to the alkaline condition of water, which acts as a buffer and causes the insoluble and finally precipitate. Or it can be due to the lower concentration in the bedrock, which can be obtained by surveying the geology of the bed of the wells. The variation in metal concentration among the sampling sites may be due to the age of the well. So up to the Sistan dam, where the wells are older, the concentration of elements is lower. It may be due to river flow, which can cause dilution of metals in wells during high water season. According to the amount of salinity and EC, irrigation with water can cause a quality decrease of soil that leads to loss of cultivated area. Moreover, the fact that water is not suitable for livestock and alternative sources should be introduced.

Given the importance of water resources, especially groundwater resources, the application of appropriate management methods in quantitative and qualitative areas is well understood. The purpose of this study is to determine the most appropriate interpolation method to investigate the spatial and temporal changes in water quality in the Qaen plain aquifer. In the present study, in order to evaluate water quality, the values of 10 water quality parameters including: concentration of calcium, magnesium, sodium, chlorine, potassium, bicarbonate, sulfate, salinity, TDS and acidity ions, using IDW, LPI, GPI, OK and SK interpolation methods. It has been interpolated and researched in 2009 and 2017. The results of comparing 5 intermediate methods using RE and RMSE values showed that for most of the quality parameters studied, IDW method with the lowest error rate is the most suitable method for intermediation. It was also found that the highest error rate was related to the LPI intermediate method for the hco3 parameter with a value of 11.84 and the lowest error value was related to the IDW method for the EC and Cl parameters with a value of 0.01 . By comparing IDW zoning maps in 2009 and 2017, it was found that the concentration of most of the studied quality parameters has increased in the statistical period under study. The upward trend in Qaen aquifer in the northeastern part is more intense and the highest upward trend in this part of the aquifer is related to the concentration of calcium ions, chlorine and salinity.

The drought crisis of Urmia Lake has led to the emergence of dried-up and wind-erosion-prone beds. Therefore, it is necessary to create and accelerate the natural and artificial revitalization of vegetation to stabilize the dried-up beds of Urmia Lake. Hence, this study was planned to measure and prepare the spatial distribution map of the organic nitrogen content of the dried-up beds of Urmia Lake based on field sampling as one of the essential nutrients for the revitalization of vegetation. To this end, 192 soil samples were taken from all dried-up beds of Urmia Lake during the Summer of 2020. We then measured the organic nitrogen content of the soil samples. Then, using the ordinary Kriging method, the content of nitrogen in non-sampled beds was estimated through the values of sampled points, and its spatial variability map was prepared. The minimum, maximum, and average nitrogen content in the dried-up beds of Urmia Lake was 0.010, 0.297, and 0.143%, respectively. The nitrogen content was low (0.117 to 0.153%), and very insignificant in most of the dried-up beds and margins of Lake Urmia, which was observed in the north, northwest, west, east and especially southeast parts of the lake. Whereas, the highest nitrogen percentage was in limited parts from the south to the southwest of Lake Urmia and was 0.189 to 0.297. Based on the findings, the implementation of managerial and technical measures in order to improve the nitrogen content of a large part of the dried beds and barren lands around Lake Urmia is necessary to achieve a successful revitalization of vegetation. However, measuring and preparing the spatial distribution map of other soil components is also suggested for future research.

Sustainable soil management with a correct understanding of soil properties helps to maintain soil fertility and prevent soil degradation. This research was conducted to evaluate the potentional use of soil management zones (MZs) as an efficient method to improve the productivity of wheat cultivation. According to this, 140 soil samples were taken randomly from wheat fields. Physical and chemical properties, along with topographical and spectral indices, were used to delineate MZs. Topographic attributes were extracted from digital elevation model (DEM) map preapared in SAGA GIS 7.8.2 software from the study area. Mapping the applied variables, selecting the optimal variable using principal component analysis, and delineating the study area based on soil properties, topographic attributes and spectral indices by using cluster algorithms in combination with geostatistics are major steps for delineating management zones. The fuzziness performance index (FPI) and normalized classification entropy (NCE) were investigated for the number of MZs. The semivariogram and kriging prediction maps showed different spatial distribution patterns, with spatial autocorrelation ranging varies from weak to strong for most of the applied variables. Finally, six principal components (PCs) with an eigenvalue of more than 1 and a total variance of 76.3% were chosen for further analysis. Based on the lowest values of FPI and NCE, six MZs were identified. The mean values demonstrate the difference between the applied properties in the MZs. This study showed that the delineation of MZs can be effectively used in soil management for cultivation crops to maximise crop production.

Drought is one of the main and repeated features of different climates and also one of the environmental events and an integral part of climate fluctuations. Monitoring systems are very important in developing plans to deal with drought and its management, and for this reason, available quantitative indicators are used to express this phenomenon. Since these indices are calculated pointwise, it is necessary to process them spatially. In this regard, mediation methods including geostatistics are used. The analysis and how the meteorological drought changes over time, as well as the ability of kriging, cokriging, image distance weighted (IDW) and radial function (RBF) methods, were evaluated in the spatial analysis of meteorological drought in the entire Karun large watershed. Drought analysis was done using rainfall data from 58 rain gauge stations and 11 synoptic stations with a statistical period of 30 years in the period from 1987 to 2016 using the standardized precipitation index (SPI). Then drought maps were prepared and evaluated. The obtained results showed that the lowest value of the root mean square error (RMSE) related to the cokriging method with a value of (0.32), and the radial function method with a value of (0.34), and this method is considered the best method for the year 2013 is Also, in the year 2008 most stations were involved in very severe drought and year 2000, most stations were involved in severe drought. The results showed that the Cokriging method has higher accuracy than other methods in drought zoning.

In precision agriculture, a productivity rating system is a significant tool to quantitatively assess soil quality. An experiment was conducted in Bilavar, Kermanshah to evaluate the spatial variability of physical indicators of soil quality of a rapeseed (Brassica napus) field. Spatial variability analysis of soil physical properties measured on a rectangular grid (100 m×100 m) was carried out using a geostatistical analyst extension of Arc-GIS software. Five physical soil quality indicators including bulk density (BD), non-capillary porosity (NCP), field saturated hydraulic conductivity (Ks), available water retention capacity (AWC), and organic carbon (OC) were determined. The physical rating index (PRI) at each sampling point was determined by multiplying the rating values for all five parameters. Results revealed that major ranges of semivariogram for Ks and AWC varied between 137-145 m and for BD, OC, and NCP they were relatively long (161-205 m). Clay and NCP showed moderate spatial dependence (0.68 and 0.28, respectively) whereas the rest of the parameters showed weak spatial dependence. Also, the correlation between PRI and the biological yield of rapeseed was fairly good (R2=0.68). Investigation of zoning maps of soil physical properties showed an increase in BD and a decrease in AWC and NCP parameters depending on changes in soil texture and organic matter content in some parts of the field. In general, the PRI index is an important tool in the quantitative assessment of soil physical conditions, and based on it and zoning maps can improve the physical quality of soil in agricultural fields.

Creating land fertility maps are especially important in terms of determining the areas that need particular nutrients, optimizing the use of agricultural fertilizers, and facilitating the optimal management of soil and plant nutrition. Spatial changes in soil and plant nutrients are common, but knowing these changes is essential for accurate planning and management, particularly regarding agricultural lands. This research aims at zoning the spatial distribution pattern of nutrients, aka nitrogen, phosphorus, potassium, sulfur, calcium and magnesium, in the soil and leaves of Dezful orange orchards trees using the Gaussian model and geographic information system (GIS).

A total number of 130 sampling points are set on the map in the vicinity of orange orchards of Dezful City with an area of 3200 hectares. Factors such as soil, cultivation and irrigation system, slope, elevation, and the manner of orange trees growth are considered to determine sampling locations. Following sampling the soil (0-60 cm depth) and plants, the samples are transferred to the laboratory and the concentration of the most consumed nutrients is measured. After preliminary statistical analyzes on the data, the correlation level of the variables that are measured in the soil and leaves of orange trees, are calculated with the Pearson correlation test. The location of sampling points is simulated via Gaussian model by using the R software. The interpolation is computed using simple kriging and kernel methods. The model sensitivity analysis for the changes applied in the base values for implementing the algorithm, is done based on the replacement of the desired values from the posterior functions as well.

Analysis of dispersion indices show that the highest coefficient of variation is related to phosphorus element in soil and nitrogen element in leaf samples. The results illustrate that the mean square error values for elements of nitrogen, phosphorus, potassium and sulfur are calculated respectively as 0.171, 0.152, 0.132 and 0.153 in simple kriging in soil, and as 0.212, 0.152, 0.229, and 0.166 in kernel method in soil; and respectively as 0.121, 0.188, 0.116 and 0.131 in simple kriging in samples of orange tree leaves, and as 0.184, 0.206, 0.172 and 0.229 in kernel method in the leaves samples as well. The results of the spatial distribution pattern of each of the measured elements in the soil and leaves of orange trees demonstrate that the lowest amount of nitrogen is in the south of the region (0.42 to 1.33 mg/kg) and its distribution pattern is similar to the distribution in the leaves of orange trees (0.9 1 to 1.29 mg/kg). Magnesium has the lowest in the north and part of the south (3.11 to 4.57 mg/kg), and sulfur in most soil of the region is between 21.31 and 26.25 mg/kg.

In examining the effectiveness of the Gaussian statistical model in the distribution of nutrients in the soil and leaves of orange trees in the gardens of Dezful city, the results display that the calculated Pearson linear correlation coefficient  has the highest correlation between calcium and potassium, as well as magnesium and calcium in the soil, but there is no linear correlation between any of the nutrients in the leaves of orange trees. In estimating the best interpolation method, calcium element in soil has the least error in both kernel and simple kriging methods, whereas in plant leaves, magnesium in kernel method and potassium in simple kriging method have less error. The highest error for soil and plant is related to potassium and calcium respectively, in the Cornell method.

 order to study the spatial patterns and prepare spatial distribution maps, point data are generalized to the surface by moving away from the site in the interpolation process. The values ​​of indefinite points are estimated by using the measured values at definite points. There are different methods for spatial data interpolation and any major defect in the spatial distribution of climatic factors and slight inattention to the appropriate method of interpolation method can lead to errors in the design estimates. So far, many researches have been done in the field of climate data interpolation in the world. The aim of this paper is determining the most appropriate method of rainfall extension pattern and fitting the best distribution function to the annual average rainfall data. In addition to the performances of different interpolation methods, the choice of the optimal model for zoning rainfall amounts in different time periods is the goal of paper.

Iran is located in West Asia and borders the Caspian Sea, Persian Gulf, and Oman. With an area of 1,648,000 square kilometers (636,000 sq. mi), Iran ranks seventeenth in size among the countries of the world. The study area is located between 32° 25' 14.67" N and 53° 40' 58.86" E. Iran has a variable and continental climate in which most of the relatively scant annual precipitation falls from October through April. The average elevation of this plateau is about 900 meters (2,953 ft.), but several of the mountains that tower over the plateau exceed 3,000 meters (9,843 ft.). Volcanic Mount Damavand, 5,610 meters (18,406 ft.), located in the center of the Alborz, is not only the country's highest peak but also the highest mountain on the Eurasian landmass west of the Hindu Kush .The monthly precipitation data from 79 synoptic stations with at least 30 years during the period from 1988 to 2017 were used. Very severe drought years and SPI values identified in the intervals of 3, 5 and 8 months until the end of May were extracted from monthly rainfall data in each year and in three ten-year steps. In order to determine the distribution of rainfall in 10-year time periods in the lack of data points, the indices of mean absolute value of relative error (MARE), mean bias error (MBE), mean absolute percent error (MAPE) and the root mean squared error (RMSE) has been used.

In general, using the above errors in the quarterly, five-month and eight-month periods between definitive interpolation methods, radial basis functions (RBF) and local polynomial interpolation (LPI) compared to the other two methods (IDW, GPI) has provided proper results that is consistent with the results of Misaghi (2006) and Eivazy (2012). Regarding the method of radial functions, the quadratic interpolation method had the lowest correlation for all variables except rainfall with a period of 3 months. Kriging methods have given better results compared to IDW method, which is consistent with the results of Soleimani et al. (2004) and Zabihi et al. (2011). The methods of kriging, simple kriging method (second-order logic) in the interpolation parameters of mean rainfall, (3 and 5 months), simple kriging method (exponential) in the interpolation variables precipitation with a period of 8 months were the best methods. Therefore, kriging method has presented relatively more accurate results, which is consistent with the results of Mirmousavi et al. (2010).

The results showed that the most droughts occurred in the second decade of the third decade, the trend is still less severe drought is occurring and the lowest is in the first decade. In order to study the effect of SOI and NAO on drought and wet years in the country, the correlation matrix was determined to investigate the drought and wet periods. Then, geostatistical modelings were performed to describe these effects. Finally, based on the study of stations and zoning models, it can be stated that the Lanina phenomenon has a little effect on drought and annual rainfall and the El Nino phenomenon has a relatively greater effect. The results show that GPI and IDW methods are not considered as good performance. These methods are based on the fit of polynomial functions on spatial data. According to the results obtained in every area and for different parameters, a single method cannot be selected as the most appropriate method and the most appropriate method can be selected. Based on the results, the most appropriate choice depends on the characteristics of the regions and a single method cannot be selected for all regions. The results show that GPI and IDW methods are not suitable and modeling in these two methods is based on fitting polynomial functions to spatial data. RBF and LPI methods have provided better output rather than other methods because the results of the 3 and 5 month periods were above 62% and the 8 months were above 72%.

Groundwater monitoring networks provide important data which are necessary to understand the dynamics of hydrogeological systems. Since the cost of the installation and maintenance of groundwater monitoring networks is extremely high, optimal design and the assessment of the effectiveness of the monitoring networks is necessary. This paper presents the application of a newly developed geostatistical method based on the acceptance probability concept, to optimize the existing network of observation wells in the Shirvan alluvial aquifer, located in North Khorasan province. To this aim, by choosing a suitable semi-variogram and using ordinary kriging, the acceptance probability in the aquifer was calculated. Then, based on the spatial pattern of groundwater level, the acceptance probability was calculated for various parts of the aquifer and the acceptance accuracy values were analyzed at different levels of probability. The results showed that based on the existing observation wells network, 19.2% of the aquifer area has a very high acceptance accuracy. On the other hand, by modifying the network of existing observation wells and adding suggested points, 46.7% of the aquifer area will have a very high acceptance accuracy. Therefore, the installation of new observation wells in the proper locations will increase the accuracy acceptance and improve the efficiency of the observation well network.

Maximum sprinkler flow rate, maximum irrigation interval, and leaching requirement are the primary factors in designing sprinkler irrigation systems, which are determined based on the physical and chemical properties of water and soil. This study aimed to prepare the spatial maps of the mentioned parameters in the plains of Islamabad-Gharb district, Kermanshah province. In previous studies, kriging and IDW methods have been found to be suitable for zoning the physical and chemical properties of groundwater and soil. Asadzadeh et al. (2019) investigated the spatial changes of groundwater quality indicators in Ardabil plain by kriging method to investigate the water situation in this area for irrigation systems. Yonesi et al. (2020) also zoned some critical water quality parameters (such as nitrate) of the Najafabad aquifer using the kriging method. Kumar and Sangeetha (2020) used the IDW method to classify groundwater quality in the study area and classify it according to drinking and agricultural uses. Karami and Basirat (2015) evaluated the spatial variations of some surface soil characteristics of Arsanjan plain by kriging and IDW methods. on the other hand, a specific method can not be considered the best method for intermediation and zoning of these parameters. In this study, both methods were used for mediation.

Awareness about spatial distribution of soil properties plays an important role in the system of crop growth and yield in the agricultural field. In this regard, genetic algorithm was used to increase the accuracy in spatial distribution of soil pHwhich combines the estimates of linear regression, gene expression programming (GEP) and geostatistics (kriging interpolation) with data related to some parts of East Azerbaijan province (Bonab,Maragheh and Ajabshir). The linear model of kriging interpolation had the minimum error. Gene expression programming and linear regression had the lowest and highest error for soil pH spatial distribution estimation, respectively, for example the mean square error, root mean square error, relative root mean square errordecreasing from linear regression to geostatistics was 47.67%,27.58%, 26.47% and scatter index and adopted mean absolute percentage error decreasing from linear regression to GEP was 23.8% and 37.03%, respectively. The use of genetic algorithm in combination method reduced the error of spatial distribution compared to the tree types of models, for example mean square error, root mean square error, relative root mean square errordecreasing from GEP to combination method was 23.33%, 11.76%,10%, respectively. The estimates of soil pH with combination method are in the alkaline range, which is consistent with the obtained data.  The minimum and maximum value of the absolute difference between measured and estimated data were at point in Bonab (0.09) and Ajabshir(0.25), respectively. The mean absolute percentage error of combination method was in acceptable range and this shows the efficiency of combination method for soil pH spatial distribution estimation.

Knowledge of the distribution of heavy metal concentrations in different components of soil particles is significant to assess the risk of heavy metals. The objective of this study was to evaluate some pollution indices and spatial variations in their estimation in different components of soil particle size fractions (<2000 and> 63 μm) in the Baghan watershed in the southeast of Bushehr province with an area of about 929 square kilometers. The location of 120 surficial composite soil samples (0-20 cm) was determined by using the Latin hypercube method. Soil pollution was assessed using geochemical indices of contamination factor (CF) and pollution load index (PLI). The kriging method was used in the Arc GIS software to interpolate the spatial variations of CF and PLI. Based on the results, the CF displayed the particles in the size < 2000 microns compared to all metals in moderate pollution conditions (1≤CF <3) and with the fineness of soil particles (particles with a diameter <63 microns) concerning to Cd metal shows significant contamination status and moderate pollution with other metals, respectively. CFZn, CFCu, and CFFe in particle size <2000 microns and CFPb in finer class were fitted with a spherical model and other metal contamination coefficients with an exponential model. CFCd and CFFe have the highest impact ranges at <2000 and < 63 microns, respectively. The results of this research confirm that corrective operation is needed to monitor cadmium status in the studied area.

Considering the importance of assessing the status of groundwater quality for industrial and agricultural uses, this study investigates the quality of groundwater in the South Khorasan region and examines the spatial continuity of the variable electrical conductivity in this region. Isatis and Surfer softwares were used to graphically display the changes of qualitative parameters in the study area and to perform sequential Gaussian simulations to investigate the spatial continuity between the electrical conductivity variables. After normalizing the data, modeling was performed, so that first the variogram was drawn and then the appropriate model to fit the experimental variogram based on the least SSR error, using cross-evaluation of the best variogram model among the models (spherical, power, cubic, etc), Selection and modeling were performed. After the modeling stage, Gaussian simulations were performed and the spatial continuity map of the electrical conductivity variable was presented. The results of preliminary statistics showed that in terms of electrical conductivity classification for salinity, most sampling areas have an electrical conductivity of 2250-750 and above 2250. Next, the spatial coherence of the variable was investigated. The probability map of values with an electrical conductivity higher than 750 μM / cm showed that most areas have high electrical conductivity. The study of preliminary statistics on the classification of sodium content in agricultural waters in the study area also showed that the sodium percentage of most sampling points is between 40 and 60%.

Spatial variations in the qualitative indicators of groundwater resources are among the factors influencing the policies of harvest management and consumption of these resources. In arid and semi-arid climatic zones, groundwater is one of the important resources for exploitation for various purposes, especially agriculture and drinking and importance study of its quality indicators in order to assess the possibility of using water resources. In this research, the accuracy of kriging and cokriging statistical methods as well as definite inverse weighted distance methods and radial base functions to predict the spatial distribution of groundwater quality parameters including chlorine, sodium, magnesium, calcium Sulfate, sodium adsorption ratio, total solute concentration and electrical conductivity were investigated in Turkmenchay region. In order to evaluate the results, cross-evaluation method and MAD, RMSE and R indices were used. Based on the obtained results, the cokriging method is the best method for zoning all the studied indicators. RMSE values for electrical conductivity are 130.67 μmohs/cm, total solute concentration is 61.13 mg/lit, calcium concentration is 0.84 meq/lit, magnesium concentration is 0.53 mg/lit, sodium concentration equal to 0.63 meq/lit, chlorine concentration equal to 0.41 meq/lit, sulfate concentration equal to 0.58 meq/lit and Sodium absorption ratio was obtained as 0.31 meq/lit were reported. Also, the evaluation of the results obtained from the spatial distribution of indicators showed that the groundwater resources in the whole study area are in a favorable condition for use for various purposes, which is more evident in the northern half of the study area. Due to the trend of changes in the concentration of elements in the area of Khajeh Ghias village, in order to improve the conditions and provide policies for extraction and consumption of groundwater in order to prevent a decline in water quality, it is recommended to monitor water quality regularly.

Due to the complexity of the nature of groundwater systems as well as the limitations of borehole drilling, temporal and spatial modeling of groundwater levels are not readily possible. Artificial intelligence methods such as RBF, ANN, SVR, ANFIS and ARIMA modeling and their combination with geostatistical methods have been used to find useful solutions for spatial prediction of groundwater level. The case study of this study is Semnan plain. The first step is the temporal modeling of groundwater level using different methods. The results showed that the ANFIS model provides more accurate prediction of the monthly groundwater level in the experimental stage than other methods (R2 = 0.994 and RMSE = 0.041). Next step, the ANFIS output data is used as input data for the geostatistical model and the linear kriging model is selected as the best model for spatial development of groundwater level. (R2 = 0.8889 and RMSE = 2.376). The results of this study showed that the combination of ANFIS model and linear kriging model is an appropriate method for temporal and spatial prediction of groundwater level.

Due to the spatial variations of evapotranspiration, and lack of adequate point estimations using interpolation methods for spatial analysis is necessary. The aim of this study was to estimate the regional values of reference evapotranspiration (ETo) in Kermanshah province, west of Iran and comparison with Iran National Water Document. For this purpose, daily weather data of 38 meteorological stations of Kermanshah and neighboring provinces were obtained from the Iran Meteorological Organization. Three methods were used for calculation and zoning of ETo, based on Penman and Penman-Monteith equations. In the first and second methods, the meteorological data were interpolated and then ETo was calculated using the interpolated data, while in the third method the point estimation of ETo (weather stations) were interpolated. For the purpose of interpolation, the CGMS software was used in a gridded network of 366 cells with 10 × 10 km size. The results showed that there exist a significant spatial variations ETo across the province. In general, in the western regions of the province, with higher temperature and lower altitude, higher values of ETo was observed. The ETo estimations based on Penman-Monteith method were higher than Penman method. Although the ETo values presented in the Iran National Water Document are calculated by the Penman-Monteith equation, but obtained amount for plain regions of the province are more consistent with Penman- equation estimations.

Rainfall data are required for planning, designing, developing and managing water resources projects as well as hydrological studies. Some previous studies have suggested increasing the density of the rain gauge network to reduce the estimation error. However, more operational stations require more installation costs and monitoring. Some common techniques including statistical methods, spatial interpolation, information-based theory and combination are used to evaluate and design the network. Chaharmahal va Bakhtiari province is a mountainous region; hence, a denser rainfall network is expected in this mountainous environment. The aim of this study was to evaluate the condition of rain gauge stations in Chaharmahal va Bakhtiari province using two approaches, i.e. geostatistical methods and entropy theory.
The main required data set for this study is a time series of rainfall data. These data were collected on a daily scale from the Regional Water Company of Chaharmahal va Bakhtiari. After performing statistical tests, the annual data series was prepared for 46 rain gauge stations. A statistical period of 2000 to 2016 was used. The homogeneity of data was investigated by double mass test and histogram drawing methods using Excel and SPSS software, and the existence of trend in the time series of data was investigated by applying a Spearman test. Then, the adequacy of rain gauges in the gauging network was investigated. Annual rainfall interpolation maps and their standard error maps were prepared using the kriging method. Contribution of each station in reducing or increasing the error in the rain gauge network was investigated by removing each station in a cross validation procedure. The efficiency of the rain gauge network was evaluated using the concept of discrete entropy and the values of entropy indices. The value of keeping the rain gauge stations was determined using the net exchange information index.
There was no homogeneity problem and significant trend in the data series. Considering the permissible error percentage of 5%, there is a need to add 15 new rain gauge stations to the network. To apply the geostatistical method, we applied it once without deleting any station; then, the kriging interpolation error was calculated for the precipitation data. Then, only one station was removed at each stage, and both the error and the contribution of each station in increasing or decreasing the error compared to the case without Station deletion were obtained. The results indicated that Ab-Turki, Shahrekord, Borujen and Barez stations were more important than other stations. Two stations namely Chaman-Goli and Ben stations can also be considered as the influential stations in error due to the density of stations in the region and error maps. Similarly, the results of the entropy theory method were found effective in evaluating the design of the rain gauge network. The highest value of H(x) was observed in the data of Armand station (3.26) and the lowest value was observed in Abbasabad station (2.28). Since H(x) shows the uncertainty of measuring data, the maximum and minimum uncertainty were found for Armand and Abbasabad sites, respectively. Based on the Net Exchange Information Index, Bardeh, Bareh Mardeh and Dezkabad stations were ranked 1 to 3, respectively, indicating that they transmit and receive more information than other stations. On the other hand, a number of stations including Dorak anari, Abtorki and Chelo stations had the lowest values.
Due to the vast extent of the area and also considering the permissible error percentage of 5%, the number of the stations in this area was found to be insufficient. Thus, although calculating the kriging error maps showed that some stations do not have a significant share in increasing the error, removing the stations is not recommendable. Regarding the new stations, new 15 rain gauge stations are needed to check out the error maps. According to the field observations, the higher priority should be given to the northwestern area (which had the largest interpolation error) in the first place. For the regions with lower error, such as northeast, east, southeast, west and southwest that do not have rain gauge stations, additional rain gauge stations should be constructed.

Simulation in groundwater flow is important for agricultural use and access to high quality groundwater.In many arid countries of the world, especially Iran, the main source of water supply is groundwater reserves.This study, using geostatistical analyzes, has investigated the environmental effects of heavy elements of Gol Gohar Goljan iron ore mine in Sirjan.For this purpose, heavy metal elements (iron, copper, chromium, cadmium, antimony, arsenic) were sampled from 115 groundwater samples.Then, by drawing a histogram, preliminary statistics and exploratory-spatial information obtained from the data of the study area were examined.The experimental half-change of each parameter was calculated using isatis software and fitted with Gaussian, spherical, linear and exponential models.After variography, Gaussian simulation in a block model prepared in the study area was performed 100 times and the map obtained from Gaussian simulation of each of the mentioned variables was prepared. In addition to examining the spatial coherence of the variables, the probability and uncertainty map was prepared according to the EPA drinking water standard and the points where the concentration of heavy metals was within the allowable drinking water limit or above the threshold were determined. The validation results were finally compared with the simulation results and the original values. The results of the studies showed that the highest risk of contamination of heavy metals iron, copper, arsenic and chromium, according to the EPA drinking water standard, is related to Gol Gohar iron ore mineral areas and long wells in Kefahnamak, Khairabad and Qatarbaneh area. Also, the results of studies on the element antimony showed that most of the groundwater resources in the study area are contaminated with this element and are not drinkable according to the EPA limit. Groundwater at points KH1, KH2, KH3 and CHD20 had the highest concentration of cadmium, and probability maps of each element were prepared.

Successful management of groundwater resources using numerical models requires knowledge of spatial distribution of hydraulic heads and it's fluctuation, aquifer parameters and other input data. One of the most basical issues in groundwater resources management is the estimation of water table from observation well network data. In this study, three methods of artificial intelligence, geostatistics (Kriging) and IDW with evapotranspiration, air temperature, precipitation and geographic inputs were used to simulate the depth of groundwater Salman Farsi Sugarcane Plantation. The results showed that the highest simulation accuracy of groundwater depth in Salman Farsi Sugarcane Plantation was related to the artificial neural network model with the highest R2 (0/92) index and lowest RMSE and MAE (1.25 and 1.74) values. Also, among the Kriging and IDW models used, the accuracy of the Kriging model was more than the IDW model. Due to the acceptable accuracy of the results of the presented models, the water resource planner and decision maker in this field can apply this optimum interpolated groundwater depth to monitoring the spatiotemporal fluctuation of groundwater depth in this area by updating it's data.