جستجوی مقالات مرتبط با کلیدواژه "زمین آمار" در نشریات گروه "آبخیزداری، بیابان، محیط زیست، مرتع"

Awareness of spatial variations in groundwater quality parameters is an important tool for identifying the capacity of the region and land management. Jiroft is located on an arsenic belt in the country and considering the effect that arsenic has on underground water and the carcinogenic nature of this element, analysis of this element is very important. Moreover, most rural regions of Jiroft use the well water for drinking. Therefore, the analysis of groundwater in this region appears to be very important for drinking purpose.

Using geostatistical methods, the present study attempted to analyze the spatial variation of nitrate, arsenic, manganese, and all dissolved solid parameters in the groundwater of the Jiroft watershed during 2019. For this purpose, water from 36 agricultural wells and wells used for drinking in villages, as well as urban drinking water samples, were sampled in three replicates. The geostatistical methods used for zoning the above parameters include ordinary kriging (OK), simple kriging (SK), radial basis function (RBF), and inverse distance weighting (IDW) with different powers. Geostatistical methods were evaluated using the mutual evaluation technique applying the root mean square error (RMSE) and mean bias error (MBE) criteria between the actual and estimated data.

In this research, the spatial changes of nitrate, arsenic, manganese and total solids dissolved in groundwater in the Jiroft watershed were analyzed using the evaluation criteria ranking results showed that the estimation of arsenic and TDS with the spherical model of the simple kriging method had the least error and the estimation of nitrate and manganese with the radial basis function (RBF) method had the lowest error. The study showed that the spatial correlation of the quality metric of the Jiroft watershed is very high. Therefore, the error of the semivariable model of the measured data of arsenic with a partial effect of 0.00025 and a range of influence of 36.7 km was 38.4%, whereas the error of the semivariable model of nitrate with a partial effect of 0.3 and a range of influence of 28.5% was 4.9%. In the Jiroft watershed, the concentration of arsenic in the water of other areas was much higher than the permissible amount for drinking based on standard 1051 of the Iranian Institute of Standards and Industrial Research except for urban water. In addition, the concentration of nitrate in all the samples was lower than the permissible amount for drinking water, so there is no obstacle to its use.

Awareness of the pattern of spatial and temporal variations of the rainfall erosivity is essential for improving soil erosion estimates, especially considering snow cover in different months. In the Caspian Sea Basin, due to the low distance between mountain regions and rainy plain, estimation of rainfall erosivity factor without considering snow cover correction coefficients is associated with a significant uncertainty. Accordingly, the current study aims to analyze the spatiotemporal variations of the rainfall erosivity factor with considering snow cover correction coefficient in the Iranian part of the Caspian Sea Basin.

To prepare monthly, seasonal and annual rainfall erosivity factor, monthly rainfall data of 56 synoptic stations of the Caspian Sea Basin were obtained from the national meteorological organization and statistical yearbooks with a common statistical period of 20-25 years (1996-2020). As a result, the rain erosive layer in different time bases using Co-Kriging geostatistical method with auxiliary variable of height above sea level with less root mean square error (RMSE) was used as the most common evaluation index and selection of the optimal method. Then the monthly snow cover layer was prepared from MOD10CM product and the rainfall erosivity for each month was modified.

Based on the results, the average annual rainfall erosivity factor of the Iranian part of the Caspian Sea Basin was 254.61 (MJ mm ha-1 hr-1), while by considering the snow cover correction coefficient, the rainfall erosivity value was redused to 210.53 (MJ mm ha-1 hr-1). Also, Bandar Anzali and Manjil synoptic stations have the highest and lowest values of the average annual rainfall erosivity factor equal to 749.30 and 162.87 (MJ mm ha-1 hr-1), respectively. In addition, after considering the snow cover correction coefficient, the highest monthly rainfall erosivity values of 27.27, 25.81 and, 25.56 MJ mm ha-1 hr-1 were observed in April, October and, November, respectively. On the other hand, 12.81% of the studied area is in the medium to very high levels of rainfall erosivity in the northern part of the Caspian Sea Basin, which can be used to prioritize different areas in order to carry out conservation measures to reduce the effect of the rainfall erosivity factor on the amount of soil erosion and sediment yield were used.

Based on the results of the current research, it can be stated that the fluctuation of rainfall erosivity in different months and seasons is also affected by the rainfall time distribution throughout the year. Therefore, the maximum amounts of rainfall erosivity in most of the studied areas correspond to the main times of agricultural activities, i.e. planting and harvesting, which are critical times for soil erosion. Investigating the spatial pattern of rainfall erosivity in the Iranian part of the Caspian Sea Basin shows many relative changes between different regions from the plains to the highlands. Although this pattern is somewhat consistent with the pattern of climate changes from drier and less rainy areas to wetter and morerainy areas, the type of precipitation and especially snow in high areas has reduced the rainfall erosivity in these areas. Therefore, the temporal evaluation of rainfall erosivity resulting from the current research, in which the snow cover correction coefficient is applied, with the timing of the presence and density of vegetation on the soil surface, provides more reliable results for land management managers and planners, especially in the agricultural sector, to choose the right type of crops for each will place the area.

This research set out to determine the qualitative and quantitative characteristics of groundwater resources in Bam-Narmashir plain by using Geographic Information System and Geostatistics.

In this regard, the geostatistical method of Kriging and Geographic Information System was used to investigate the spatial distribution of qualitative characteristics (e.g., chlorine content, electrical conductivity, dissolved solute concentration, magnesium, potassium, acidity, sulfate, and bicarbonate) and the groundwater quality index (GQI) as well as the depth of ground water in the ten-year statistical period of 2011-2021.

The results of the preparation of spatial distribution maps of water quality variables in the statistical period revealed an increase of the value of these parameters (except for the pH variable) from the south to the north of the plain. So that from the middle of the plain to the north, this increasing trend becomes more intense. The results of preparing the difference map of GQI index during the 10-year period under investigation indicated that the quality of ground water improved in a small area of the plain and in other areas of the plain, especially in the northern parts, the difference of this index at the beginning and end this period increased by more than 400 units. This issue shows the severe drop in water quality during this period in most parts of the plain. The results of the groundwater depth change map disclosed that, except for a very small part in the middle parts of the plain, in other parts of the plain were observed a drop in the level of underground water reported to be 52 meter in the most western part of the plain and on average about 9.8 meter in other parts of the plain.

The results of this research showed that the quality of ground water was good for drinking purposes in a very small part of the south of the plain as an except, having an unsuitable for drinking purposes category in other parts of the plain. Overharvesting and lowering of the aquifer's water level has caused the rise of salt water and the interference of the salt and fresh water table in all areas of the plain especially its central areas, which the continuation of this process can increase the salinity of the aquifer to such an extent causing deterioration of all activities in agriculture and animal husbandry in the region. Furthermore, in the results of quantitative study of ground water indicated a strong and increasing trend in the amount of exploitation leading a decrease in the level of groundwater.

Evapotranspiration (ET) is one of the most important components of the hydrological cycle and an important factor in determining water demand, its spatial distribution and accurate estimation for many studies such as water balance, crop production simulation, program Irrigation planning and water resources management are very important. Also, using of geostatistical methods is one of the ways to estimate evapotranspiration in places without stations. In this study, SWAT hydrological model was used to simulate evapotranspiration of Samalghan watershed . To evaluate the simulation results, indices R2 , bR2 , NS was used. In the calibration stage, the coefficients of R2, bR2 and NS were equal to 0.74, 0.71 and 0.68, respectively, and in the validation stage, they was equal to 0.69, 0.62 and 0.59. .Then, using the results of spatial analysis of data in this study, the exponantial model was selected as the most appropriate model for zoning due to the lower RMSe value and higher correlation.

Groundwater is important water resource supply, especially in arid and semi- arid regions. Increased utilization of the ground water aquifer leads to significant reduction in the storage of reservoirs. This study evaluates the hydrogeological drought in Garmsar plain using Groundwater Resource Index (GRI). First, we used 17 piezometric wells data over 2001-2011 statistical period to calculate GRI in the beginning, middle and end of the period. So, we used different interpolation method including geostatiscal method ordinary kriging (OK), simple kriging (SK) and deterministic methods including inverse distance weighting (IDW) to prepare the maps over three periods. The mean absolute error (MAE) and root mean square error (RMSE) indices were used to evaluate the accuracy of simple kriging, ordinary kriging and IDW classification based on the drought maps. The results showed that the values of MAE and RMSE criteria for simple Kriging is better than the other methods and indicates the suitability of this method for zoning GRI. According to the results, the most severe hydrogeological drought in Garmsar plain was at the end of 2011, that 91.16 % of the study area was suffered from severe drought. SPI was used for considering the effects of meteorological drought in the time scale of 3, 6, 9, 12, 24 and 48 months on groundwater. The correlation between SPI and GRI showed long-term timescale of 48 monthly has the greatest correlation with groundwater level.

Access to real information and data on the impact of watershed management projects and their technical and economic evaluation is possible through representative and paired catchments. Since management operations and topographic characteristics have a great impact on carbon retention in ecosystems, this study aimed to determine the amount of soil organic carbon due to changes in some topographic factors including altitude, direction, and different percentages of slope and measurement and zoning of soil organic carbon before biological and mechanical watershed management operation.

For this purpose, 27 land units in a part of the rangelands of sample sub-basin with an area of 25 hectares were selected as the study area. soil samples were collected from plots with dimensions of 10 * 10 m2 per unit. Composite samples consisting of 5 soil samples were collected from the center and four corners of the mentioned plots from a depth of 0-10, Soil samples were analyzed for some soil properties; including, organic carbon, organic matter, texture, bulk density, electrical conductivity, and pH.. SAS v9.1 software was used for statistical analysis. After performing normality tests, homogeneity of variance was obtained by ANOVA analysis test. Then, Pearson correlation coefficient and linear regression analysis were used to evaluate the correlation and determine the relationship between independent and dependent variables (organic carbon) then, the evaluation of spatial changes of soil organic carbon and zoning map were investigated using the geostatistical method after testing the normality of the data, interpolation methods were used and the effective parameters were optimized. Finally, using the statistical criteria of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) the amount of deviation of the actual data was measured with the estimated data and the best method for interpolation of soil organic carbon was determined.

The results showed no significant changes of soil organic carbon in response to physiographic parameters. However, soil organic matter and organic carbon was significantly influenced by different aspects (p≤0.05). The maximum (8.46 and 55/87 t/ha) and minimum (0.9 and 22/77t/ha) organic carbon and soil organic matter were observed in north-facing and south-facing slope, respectively. Furthermore, there was a positive correlation between soil OC and organic matter and negative correlation with Ph. In order to interpolate soil OC, geostatistical and Classical statistics methods were compared considering RMSE and MAE values following by optimization procedure. The results showed geostatistical methods have lower error and higher accuracy comparing to Classical statistics methods. In summary, simple Co-kriging with organic carbon as auxiliary variable is the most appropriate one for soil organic carbon interpolation in the study area. The results indicate poor spatial distribution and low carbon sequestration in the region due to the degradation of rangelands. The exploitation and overgrazing of livestock is one of the main factors of low carbon storage potential in this basin

The results of statistical analysis in this study show that the soil organic carbon of the region is not affected by the physiographic parameters of the basin, but changes in the amount of organic carbon and soil organic matter were significant compared to changes in slope direction.. Changes in the organic carbon of the area in the slope directions are affected by changes in vegetation and soil depth. Decreased vegetation on the southern slope means the reduced entry of organic carbon into the soil. However, the highest level of organic carbon in the topsoil in the northern slope of the region is estimated at 8.46 tons/hectare, which can be said that the Gonbad watershed is generally poor in terms of organic carbon.

The main objectives of this study were to prepare a prediction map of the potential habitat of Agropyron intermedium and Find important factors influencing the establishment and distribution of this species and the preferred tendency of the species was relative to environmental factors Using the Maxent model. For modeling, region condition information was prepared including topography, climate, geology and soil, satellite images, digital elevation model (DEM), geology map, and climatology data. Then soil and plants sampling was performed and Soil samples were transferred to the lab. Soil properties were measured including gravel, pH, EC, lime, organic matter, N, K, P, sand, clay, and silt in the laboratory. Geostatistical methods were used for data analysis and mapping of environmental variables and the Maxent model was used for prediction maps. Kappa coefficient indicates that the Maxent model predicted A. intermedium habitat at a very good level (kappa = 0.85). Also, the accuracy of the classification of habitat maps predicted in the Maxent model is acceptable according to the analysis of the area under the curve (AUC = 0.771). The results showed that topographic variables and clay soil factor in the occurrence and distribution of A. intermedium has the greatest effect and increasing lime and ec have a negative influence on the presence of this species. A. intermedium is a desirable species that in addition to being used in creating hand-planted pastures, it is very important in improving and developing rangelands, especially in cold regions. Therefore, maintaining genetic and scientific,

One of the important problems of water resources studies is estimating the spatial distribution of rainfall based on point rainfall observations. In the present study, interpolation methods including definitive mathematical methods including Inverse Distance Weighting(IDW)  and Radial Basis Functions (RBF), Global polynomials, and local polynomials, and Kriging and Cokriging geostatistical methods using Geographic Information System (GIS) to estimate monthly and annual rainfall in the Namak lake watershed were evaluated. Spatial distribution analysis was carried out in the period 1988-1999 to 2006-2007 water year. In this study, in order to compare interpolation methods in iso-rainfall of the Namak lake watershed, 110 meteorological stations into the mentioned watershed with the statistical period of 30 years were used. The results showed that overall in annual rainfall according to Root Mean Square (RMSE) index, Cokriging and Kriging geostatistical methods in rank one and two, respectively have the best rainfall estimation. Therefore, Cokriging method with RMSE of 63.68mm had the best estimation. RBF, Cokriging and Kriging methods had also in the best estimation for   monthly rainfall data. In general, geostatistical methods have been overestimated in monthly and annual rainfall estimation in the Namak lake watershed.

Earth's subsidence is one of environmental hazards that humankind faces in recent decades due to the extraction of groundwater resources in the plains. Over the past years, the desertification of plains in Iran has caused a great deal of economic and social damages to the country due to the decline in groundwater levels. Marvdasht Plain Aquifer has been significantly affected by this phenomenon in recent years. On the other hand, considering the location of historical monuments like Persepolis in this village, the issue of subsidence checking is important. Therefore, the present study investigates the relationship between the amount of groundwater extraction and subsidence situation in this region. In this study, Kriging statistical method was used to study the changes in groundwater level, and radar differential interferometry and COSMO-SkyMed radar images were used to estimate the rate of subsidence. Results showed that the average annual groundwater loss is 45 centimeters. On the other hand, the highest rate of subsidence in the oblast area is between 2.5 centimeters and about 15% of the area has a subsidence of less than one cm in the period from 2013/10/01 to 2014/02/01. Comparing the map of the groundwater level and the map of the earth's surface displacement, showed that the areas with the highest groundwater abundance have the highest displacement and ground falls about one cm for every 45 cm of groundwater level drop. Comparison between amplitude and subsidence pattern obtained from the results of radar interferometry technique with location, the density of wells and groundwater abstraction in this plain shows that subsidence has occurred in the same areas where the density of these wells and extraction of groundwater resources is high.

Successive drought events as one of the most important environmental crises, along with population growth and uncontrolled water extraction, have led to an increase in the depth of groundwater, especially in arid and semi-arid regions. The purpose of this study is to investigate the geostatistical relationship between groundwater depth data and groundwater depth based on precipitation data in three observation wells located in Fars province. These wells were selected based on the PSO clustering technique. Thus, the three observation wells that were closest to the center of the calculated clusters were selected as the representative of the clusters. These wells are located in Karsia, Dolatabad, and Fatehabad regions for clusters 1 to 3, respectively. Monthly groundwater depth data has been used from 2003 to 2017. Kriging and cokriging methods were performed in the GS+ environment. In this research, precipitation data was used as an auxiliary variable. Furthermore, the models were selected based on the lowest RSS values ​​and the nearest R2 values, and the spatial structure ratio (C / C0 + C) to one. Accordingly, the selected models for the main variable (groundwater depth) in the first to third clusters are spherical, power, and linear, respectively, and for cross-variogram models (precipitation - groundwater depth) are all spherical. The results showed that in the validation and test stage, the Cokriging method has higher accuracy than the Kriging method. The test stage in kriging and cokriging methods for RMSE index are (0.92 and 0.41), (0.54 and 0.52), and (1.25 and 0.95) in 1, 2, and 3 clusters, respectively.

Precipitation estimation in areas without recorded meteorological data is of great importance in hydrological studies and flood forecasting. Due to the lack of high-altitude meteorological stations with long-term recorded data in Mazandaran province as well as stochastic attribute of rainfall data, using statistical methods based on covariates and comparison with geostatistical methods for interpolating monthly and annual rainfall data in this province is Inevitable. For this purpose, precipitation data from 21 meteorological stations over a 13-years recorded period (2004-2013) were used. In order to determination of appropriate interpolation method of rainfall data, six models including Ordinary Kriging, Cokriging, Inverse Distance Weighting, spline, three-dimensional linear gradient and regression-kriging were investigated. Evaluation of the methods was also performed on the basis of root mean square error, mean bias error and regression analysis. Variography analysis showed spherical and exponential models as the best theoretical semivariogram models. The results of error indices analysis showed the Spline model has the lowest efficiency and the three-dimensional linear gradient was found as the most appropriate interpolation model of rainfall data which in comparison with other models reduced the rainfall estimation error from 100 to 200 mm(about 40 to 60 percent). However, its accuracy is reduced in hot and humid months. Investication of rainfall maps illustrates the accuracy of covariate based interpolation methods in detecting low rainfall and high rainfall points of the province. So that high rainfall district is located on the west coast of the province and by moving east the amount of rainfall decrease. Due to the similarity of the rainfall map and the digital elevation model, it is noted that the amount of precipitation in valleys is very different from precipitation in the highlands of this province. The results of this study showed that in areas with complex topography, the use of covariates leads to a significant increase in the accuracy of rainfall maps.

Recent droughts have caused a decline in the level of groundwater aquifers, which has affected water quality and usability. The aim of this study was to investigate the effect of drought on groundwater quality in Iranshahr plain aquifer. In this study, drought rates were studied using the statistics of Iranshahr synoptic station during the 30-year statistical period (1987-2017) based on SPI index in DIP software, for 6 to 24 months’ time series. Water quality was studied for 16 piezometer wells and 6 aqueducts during 2005-2017. Also, the top four models of semi-spherical, exponential, linear and Gaussin were evaluated using GS + 5.1 software. Then zoning maps of groundwater quality parameters were prepared using the models detected in ArcGIS 10.4.1. The results showed that during the period 1987-2016, normal drought has been dominated in the plain. Drinking water quality is divided into 4 classes in wet hydrological years (2005-2006) and 5 classes in drought (2016-2017). Good quality in terms of drinking decreased from 7.53 percent to 3.92 percent. Water quality has decreased from east to west of the region and the percentage of suitable and usable classes has decreased.

Knowing the temporal and spatial distribution of rainfall is one of the most important factors in implementing soil and water conservation, flood control, and drought management projects and one of the most important parameters for water resource planning. Given the significant variation in rainfall on the one hand and the shortage of rain gauge stations to record rainfall, on the other hand, the requirement of correlating the rainfall estimation model to the spatial is inevitable. In this regard, geostatistics is one of the most important methods for estimating the spatial distribution of rainfall. The purpose of this study was to evaluate the geostatistical methods in annual, monthly and maximum 24-h rainfall estimations in the Haraz Watershed in Mazandaran province and to study their spatial variations in the time period of the water year 1976-1977 to 2013-2014. In this research, 40 meteorological stations with 38-year statistical period into and around the mentioned watershed were used to compare the geostatistical methods including Kriging, Co-kriging, inverse distance weighting and radial basis function in the isohyetal map for the Haraz Watershed. For this purpose, using the cross-validation method, by removing the individual stations, their precipitation values were determined and compared with the observed values. The results showed that radial basis function for annual rainfall and simple Kriging for maximum 24-hour rainfall, and simple and ordinary Kriging, Co-kriging, inverse distance weighting and radial basis function for different monthly rainfall is the most appropriate method. There is the spatial rainfall dependency on a monthly scale from 11.6 to 99.2 km, annual scale with 35.9 km and maximum 24 hour rainfall with 21.3 km. These results can be used to design distance among meteorological stations for rainfall measurement.

The aim of this study was to predict the habitat expansion of Festuca ovina by maximum entropy method and to determine the effective factors on its spread in the elevation gradient of Moghan-Sabalan rangelands of Ardabil province. The 28 study sites were selected and in each study site 3 transects and along each 100-meter transect 10 plots of 1 1m2 with a distance of 10 meters from each other were selected for sampling. Soil sampling in each study site was sampled from two depths of 0-15 and 15-30 cm. Soil properties using spatial statistics, topographic characteristics using digital elevation model, climatic information using meteorological data of the region, and land use measurements prepared with the help of a user map. Prediction maps related to the distribution of F.ovina were prepared using the maximum entropy modeling method. The role of environmental factors affecting the distribution of the species was investigated using the Jackknife method and response curves. Height variables, slope, patch size (coefficient of variation), potassium (15-30 cm depth), and lime (0-15cm depth) had the highest share in the model. The area under the curve index (AUC) was 0.86, which indicates the accuracy and average efficiency of the model in identifying the most desirable distribution areas. The kappa coefficient obtained from the comparison of forecast and actual maps was 0.72, which is at a very good level.

In recent years, due to the expansion of industrial activities, the concentration of heavy metals in the environment as well as foods has increased. Heavy metals are dangerous because of their bioaccumulation. Regarding the importance of contamination of heavy elements of the soil, the present study aimed to provide the spatial distribution of heavy metals and its relationship with land use in the Yazd-Ardakan plain, Iran. First, 201 soil samples from depths of 0 to 20 cm were sampled using the hypercube method, and the total concentration of iron, manganese, nickel, lead, and zinc elements were determined using Analytical Jena-novAA300 atomic absorption device. Then, to convert point data to surface data, geostatistical methods of IDW, GPI, RBF, LPI, and Kriging were used. The land cover/use map of the Yazd-Ardakan plain in 2016 was mapped using an object-oriented classification method. Results of the relationship between heavy metals concentration and land cover/use showed that the agricultural lands and gardens and sand dunes with the mean of 0.950 and 0.836 ppm had the highest and lowest iron concentrations. The highest mean concentration of manganese was related to the residential land (1.821 ppm) and the lowest mean of rocky terrains (1.083 ppm), the most average for poor rangelands and bare land was (0.302 ppm), Residential areas had the lowest nickel concentration (0.192 ppm). The highest mean of lead metal in agricultural land and gardens, as well as residential areas (1.465 and 1.373 ppm, respectively) and the lowest, mean in rocky terrains (0.925 ppm). Agricultural and gardens areas, and residential lands, have the highest mean of zinc concentration (0.583 and 0.552 ppm, respectively), and the rocky terrain has the lowest zinc concentration (0.342 ppm).

Rainfall erosivity is usually expressed in terms of erosion indices based on rainfall characteristics. Various indices have been presented so far, among which the erosive factor (R) in the global soil loss equation (RUSLE) is more popular in the world. The aim of this study is modeling of rainfall erosivity index using 27 sinoptic station and trend analysis with Mann-Kendal test during 1951-2014. A modified Fornier index, which can be calculated on the basis of monthly rainfall, was used by reviewing worldwide research and also by the lack of stations. After calculating the rainfall erosivity factor, the rainfall erosivity map was mapped and semi-variogram map with GS+ software was used to show the spatial correlation. For interpolation of erosivity index was used from IDW, GPI, LPI and Krigging. Root mean square error indices and mean absolute error values were used to select the best mediation method. The results showed that the Kriging method with root mean square error of 0.8 and absolute mean error of 0.8 is the best method. Thus, the country can be divided into five regions which semi-arid regions have the highest rainfall erosivity. Also, these regions have a increasing trend of rainfall erosivity

The ecological, economic, and social potential of an area for large and large uses is influenced by the quantity and quality of the waters. Therefore, appropriate methods of surface water and groundwater have been investigated qualitatively and quantitatively in order to use its results in assessing the power of the land. One of the important factors for sustainable development is the availability water resources for different uses, which imposed its quality is very important. Nowadays, groundwater resource management plays the main role in arid and semiarid regions. Investigations on the spatial variations of Water Quality Index (WQI) are very important to determine the best management program. Geo-statistical methods and ArcGIS software can be useful for this purpose. The aim of this study is WQI zoning for various uses (drinking, agriculture and industry) in the Silveh watershed (West Azerbaijan province).

In this research, water quality zoning based on WQI method for different drinking uses (pH, TDS, HCO3, Cl, Ca, Mg, K, Na and SO4), agriculture (EC, SSP and Cl) and industry (pH, TDS, Cl, TH and SO4) were sampled from 145 points (springs) from the basin level representing the studied area, which was carried out in July 2012. Then, to measure the parameters, the samples were transferred to the laboratory of the Faculty of Natural Resources of Tehran University and tested and the parameters were measured. In this research, we try to compare different methods of interpolation and select the best method to base the groundwater quality zonation map using the WQI index. Initially, the WQI index was calculated for all sampling points. After calculating the water quality index, the zoning water quality in the area was used Inverse Distance Weighting, Global Polynomial Interpolation, Local Polynomial Interpolation, Radial Basis Function and Kriging methods. Geostatistical methods to evaluate and select the best method of ArcGIS is the ability to perform cross-validation techniques and statistical criteria Root Mean Square Error (RMSE) is used.

Water Quality Index (WQI) zoning in the Silveh watershed for various uses showed that this area has no limitations in terms of groundwater quality and the use of groundwater for drinking, agricultural and industrial uses. The lowest and highest water quality index for drinking water in the area was 33.08 and 38.67, respectively, which is in the high-class of water quality index (less than 50). The lowest and the highest water quality index for agricultural consumption in the area was 36.27 and 72.77, respectively, which is in the high and good class of water quality index (less than 50 and 100-100), and There is no limit to agricultural consumption. Also, the lowest and the highest water quality index for industry consumption in the area was 34.32 and 64.96, respectively, which is in the excellent and good water quality index (less than 50 and 100-100) respectively and for the unlimited use of the industry.
 

Based on mountainous conditions and limited human activities (except for the southern and eastern areas of agricultural activities), the quality of water in the area is good. Although it is growing from the southeastern part of the city of Piranshahr, measures should be taken to prevent degradation of groundwater quality in the area due to various activities. Land use surveys show that agricultural use is about 19.97%, good rangelands are about 50.66%, good poor rangelands, 6.33% of the basin, and county of Birland and residential land cover 23.27% and 34% of the area respectively. The results also clearly illustrate the distinction of land use in the catchment area, because the water quality index in the agricultural and vegetation areas is in a good class (100-150). Given that 50% of the area is covered with good pastures (these rangelands do well to clean up underground water). The results of this study confirm the field performance in terms of high performance and water quality indexes, and recommends that similar research be used. Of course, the lithology of the area should not be ignored. Because water quality has a high correlation with the region's mineralogy. The existence of calcareous and dolomitic stones in the studied area is also a reason for the good water quality of the area.

As the most valuable natural resources, protection and management of groundwater is vital. One of the best ways to protect groundwater resources is monitoring groundwater quality spatial variationsand then managing the water resources exploitations and the land uses. In this research, the spatial distribution of groundwater quality variables and the optimize water well network with acceptable precious in Shahrkord Aquifer is evaluated. According to the cross validation and the values of error evaluation criteria, the universal kriging model with linear trend, the ordinary kriging model with exponential semiovariogram, the Log ordinary kriging model with exponensionalsemiovariogram, the universal kriging model with linear trend with exponential semiovariogram, the log ordinary kriging model with panta spherical semiovariogram and the ordinary kriging model with exponential semiovariogram, has been selected for EC, Ca2+, Cl- and Mg2+, HCO3-, SO42- and Na+ and NO3- respectively. The quality-zoning maps of the Shahrkord Aquifer have been prepared. The results from the zoning quality map indicate that the groundwater quality reduced in central parts of the aquifer. In addition, in this research, to obtain an optimum monitoring well network was applied omittingmethod. Out of 35 observation wells, identified and removed six wells from the network. Using adding well method identified 11 wells as optimal points and added to the network.

Groundwater quality, especially siltation and corrosion, are of great importance considering economic and health concerns. Due to the growing population and increasing water demands in the Hable-Roud river basin and therefore increasing importance of groundwater, this research aims to assess the potential of corrosion and siltation in groundwater resources. In this research, the Langelier Saturation Index, Ryznar Stability Index, Puckorius Scaling Index and Aggressive Index indicators were used for corrosion and siltation assessment and geostatistical analyses tools in ArcGIS were applied for zonation of the indicators across the watershed. Given the average values obtained (0.67 for LSI; 6.51 for RSI; 6.09 for PSI; 12.89 for AI), a large portion of the study area (about 74%) is faced with a siltation condition. Considering the minimum values of RMSE, the two indices of LSI and RSI were interpolated using the Kriging method, while the IDW and RBF were considered for the interpolation of the PSI and AI indices, respectively. Results suggest that, based on the SI indicator, 75.3% of the total area encounters a siltation condition while the figures change for AI as 72.96%, for PSI as 56.42% and for RSI as 32.17%. In general, assessment of the corrosion and siltation indicators indicates a siltation status for the groundwater resources of the basin, which necessitates developing a suitable management plan to prevent its economic and health consequences. One of the appropriate ways to deal with the siltation issue is to identify critical areas and then adjust water pH to under saturation level.

Rainfall variability and its impact on water resources are important climatic issues. Intra-annual variations in rainfall are characterized as precipitation concentration index (PCI). Another feature that directly correlates with the concentration of precipitation is the rain erosivity (Modified Fournier Index MFI). The aim of this study is to investigate the PCI and MFI indices and map their spatial variations. The Modified Fournier and precipitation Concentration indices were calculated using rainfall data from 55 meteorological stations located in Khuzestan province. To produce the spatial variations map, point information is converted to regional using geostatistical and deterministic methods. The results indicated that the ordinary and simple Kriging with Gaussian type have highest accuracy for interpolation of the PCI and MFI indices, respectively. According to the spatial variation map of the PCI, the highest values of that index are seen in the southern region and the lowest values are in the north and northeast of the Khuzestan province. The PCI values ranged between 20 and 31 indicating severe seasonality. The MFI values vary from 49 to 224 in which the highest values are for the northeast region of the province and the lowest ones are for the south and southwest zones. Generally Rainfall limited in a certain months.alues are for the northeast region of the province and the lowest ones are for the south and southwest zones. Generally Rainfall limited in a certain months.