جستجوی مقالات مرتبط با کلیدواژه « درون یابی » در نشریات گروه « آب و خاک »

Soil desalination in drained fields is very important because soil salinity harms on crop yield. Therefore, for the optimal management of water and soil, and the sustainability of agricultural lands, salinity monitoring in soil is essential. In this study, 44 soil samples from the depth of 0-30 and 30-60 cm were prepared from drained lands, during one cropping year. Then, soil salinity and acidity map was produced by GPI, LPL, IDW, Ok, RBF methods. Evaluation of salinity at 0-30 cm showed that stable kriging in the first and third months, exponential kriging in the second month, RBF inverse multi-quadrant model in the fourth and fifth months, were the best models. The results 30-60 cm showed that stable kriging in the first and third month, Gaussian kriging in the second month, Exponential kriging in the fourth month, and RBF inverse multi-quadrant model in the fifth month were selected as the best model. Also, evaluation of pH at the depth of 0-30 cm showed that Spherical kriging in the first month, GPI with the first power in the second and fifth months, IDW with the second power in the third month and Exponential kriging in the fourth month had the lowest RMSE. Evaluation of pH at 30- 60 cm showed that stable kriging method in the first, third, and fourth months and GPI with the first power in the second and fifth months had the lowest RMSE. The problem of salinity and alkalinity was observed in most of the surveyed region, and the level of salinity in the surface layer of the soil was higher than in the deep layer. The comparison of the salinity map in different months shows that the salinity has been decreasing during the 5 months of monitoring and the general trend of pH changes is similar to EC.

The management of water resources, especially groundwater, is of particular importance in arid and semi-arid regions. Improper understanding and unsustainable exploitation in these areas will result in irreparable damage, such as severe and irreversible groundwater level decline, reduction in well and aqueduct discharge, and changes in the pattern of groundwater flow. Therefore, the aim of this research is to investigate the decline in the groundwater levels of Mashhad Plain, which is selected as one of the most important and largest plains in Khorasan Razavi province. In order to conduct this study, the data from 62 observation wells were examined over a period of five years (2011-2016, 2016-2021). Initially, the statistical data was collected and organized using Excel 2017 software, and the hydrograph representing the aquifer of Mashhad Plain was plotted. Subsequently, using ArcMap 10.8 software and the Inverse Distance Weighting (IDW) method, maps of the groundwater level and water table for an 11-year period and two consecutive five-year periods were generated. The results obtained in this study indicate that the groundwater level in this aquifer has, on average, increased by 0.36 meters in the first five years and 0.32 meters in the second five years for the specified time period. The central, southeast, and northwest parts show the highest increase in reaching the groundwater level. Additionally, the hydrograph representing the Mashhad aquifer shows an average annual decrease of 0.33 meters in groundwater level over the 11-year period.

In arid and semi-arid regions of Iran, agriculture and animal husbandry activities have always been next to each other; where providing the livestock fodder has been found as one of the main concerns of farmers. Therefore, it seems necessary to introduce suitable fodder plants to be cultivated in these regions. First of all, the ecological needs of tagasaste species were determined based on existing liteartures. For this purpose, four factors of climate, land use, topography and irrigation water were initially mentioned as the main criterias.Based on the results of the AHP analysis, the climate (0.507) and irrigation water (0.312) criterias have had the highest relative weights for tagasaste cultivation in Yazd province.Among the investigated sub-criterias, the number of freezing days per year (0.212), the annual average minimum absolute temperature (0.158), the salinity of irrigation water (0.130) and the annual average maximum absolute temperature (0.072), were found as the main important sub-criterias for the cultivation of tagasaste in Yazd province, respectively.

Indiscriminate exploitation of groundwater has caused a decrease in the groundwater level and as a result, has caused land subsidence in many areas. This problem is especially visible in arid and semi-arid regions like Iran, where water supply for agriculture, drinking, and industry is done from groundwater water sources, and in recent years, it has seriously threatened the aquifers of the plains as a serious danger. In this research, due to the importance of the problem, land subsidence in the Damghan Plain aquifer located in Semnan province was studied and investigated.In this research, the amount of subsidence was measured at the field, and then its spatial changes were investigated using conventional methods such as Kriging interpolation, Co-kriging, and inverse distance weighted interpolation (IDW). Also, the artificial neural network model was used to estimate and interpolate the amount of subsidence. Three statistical indices namely the coefficient of correlation (R2), the root mean square error (RMSE), and the mean absolute error (MAE) were used to compare the estimation of subsidence values using an artificial neural network model, kriging interpolation method, cokriging, and IDW. To perform interpolation using kriging, cokriging, and IDW interpolation methods, the variogram of land subsidence data was drawn. Also, in order to increase the accuracy of the mentioned models in predicting the amount of subsidence, the auxiliary variable of water level reduction was used.Using different functions such as circular, spherical, exponential, Gaussian, and linear models for plotting the semivariogram, results show that the Gaussian function with segment-to-threshold ratio (C0/(C0+C)) equal to 0.26 has better performance compared to other models. Also, the artificial neural network has a better performance compared to the kriging method and the inverse weighted distance method and has been able to reduce the RMSE error value in the validation stage by 17.6% and 31.3%, respectively. It has also increased the value of the R2 from 0.502 and 0.421 to 0.721.The use of the auxiliary variable of water level reduction has increased the accuracy of the models used in predicting the amount of subsidence. In this case, the comparison between the estimation of subsidence values using the artificial neural network model compared to the interpolation method of kriging, cokriging, and IDW shows that the artificial neural network model with a coefficient of determination (R2) of 0.860 and 0.751 and 0.015 and 0.017, has a better performance compared to the mentioned methods and reduce the amount of prediction error. Therefore, the artificial neural network model can be used with good accuracy as an alternative method instead of conventional interpolation methods to investigate the spatial changes of land subsidence.In recent years, the phenomenon of land subsidence has occurred in many plains of Iran due to excessive extraction of underground water, including Damghan Plain. In this research, by using the measured data of land subsidence in this plain, its spatial changes were investigated using kriging and cokriging methods (geostatistics method), inverse distance weighted method (IDW), and weighted IDW (deterministic method) in the whole plain. it placed. The research results showed:• Compared to circular, spherical, exponential, and linear functions, the Gaussian function can better estimate the spatial changes of land subsidence.• The results of the kriging method were better than the inverse distance weighted (IDW) method.• The artificial neural network model with Gauss function and two intermediate layers has better performance than other transfer models such as sigmoid, hyperbolic tangent, and hyperbolic Bade secant.• The use of artificial neural network model has increased the accuracy of land subsidence estimation compared to conventional methods such as the kriging method and inverse weighted distance method.

Knowing the spatial and temporal changes in groundwater quality is one of the important issues for optimal management of groundwater resources. In this study, the hydro geochemical properties of groundwater resources in Miandoab plain (West Azerbaijan province of Iran) were investigated.

For this purpose, the data of 51 study wells during the time period (from 2014 to 2018) were used. The qualitative parameters such as the amount of Mg2+, Ca2+, Na+, K+, HCO3-, SO42-, Cl-, CO32- and the indexes such as TH (Total Hardness), TDS (Total Dissolved Solids), EC (Electrical Conductivity), SAR (Sodium Absorption Ratio) and pH were evaluated based on drinking, agriculture and industry water quality standards. Aquifer qualitative zoning maps were extracted by entering the descriptive layers obtained from the qualitative analysis in ArcGIS software and selecting the best interpolation method based on the validation technique.

The groundwater hydro chemical analysis based on the drinking water standards of the Energy Ministry indicated that the groundwater of the plain is in a relatively acceptable condition. This was also obtained from groundwater analysis by the Schoeller method. It was found that the largest areas of this plain had saline groundwater, in terms of agriculture. Industrially, sedimentation in the plain was more pronounced than corrosion.

Results showed that the aquifer quality declined from 2014 to 2017 and improved in 2018. Zoning maps showed that Miandoab aquifer has better quality and condition in the feeding areas in terms of drinking, agriculture and industry. In other words, according to the geography of the plain, the aquifer feeding areas were less vulnerable than the areas located in the evacuation areas.

Estimating the natural flow regime of a river is crucial for water resource management. Understanding the temporal and spatial distribution of river flow can greatly assist decision-makers in water resource management. This study aims to analyze changes in the Water Year Classification Index (WYCI) at 31 hydrological stations in Ardabil province (1974 to 2018) at different time scales and its spatial changes using GIS. The WYCI index was calculated at all hydrological stations, and the map of the spatial changes of the index was prepared using the inverse distance weighting method. According to the results, the maximum and minimum median flow values were related to the spring season (3.7) and summer (0.3) cms, respectively. The median flow values in the winter and autumn seasons were 2.5 and 1.3 cms respectively. The results showed that the average values of the WYCI index in autumn, winter, spring, and summer are 11.9, 10.7, 13.8, and 15.2, respectively. The status of the index for dry and wet periods was highly irregular (21.6) and moderately irregular (14.3), respectively. Based on the research findings, the snow-fed rivers located in the southern of Sabalan (Lay, Nir, Atashgah, and Yamchi stations) have a regular regime, while rivers in the eastern part of the province become more irregular towards summer with an increase in WYCI values. Overall, the analysis of spatial changes in WYCI indicates the existence of a unique spatial pattern at different time scales, and it is necessary to consider it in water resource management planning in Ardabil province.

The rainfall decrease and limited water resources in arid and semi-arid regions such as the Zahedan region caused the groundwater resources to need more attention in these areas. Moreover, the increase in the population in Zahedan Country and the lack of a suitable system for collecting wastewater in this area have made the pollution of underground water inevitable. So that, the excessive extraction of underground water resources and successive droughts on the other hand have caused a decrease in the underground water level in this region. Therefore, the present study was carried out with the aim of time-spatial monitoring of underground water quality in Zahedan County.

The aim of this study was to monitor the temporal-spatial quality of groundwater quality in Zahedan City in four stages. Determining the geographical coordinates of sampling wells, calculating the water quality index (WQI), and Wilcox index, comparing the interpolation methods in ArcGIS 10.8 software to prepare the spatial map of the WQI and comparing the spatial variations of the WQI. This research was conducted in two time periods 2010-2013 and 2014-2017. To investigate the temporal changes in water quality parameters, annual statistics of 90 wells and aqueducts with continuous statistics were used. Then, WQI and Wilcox’s indices were calculated for drinking and agricultural uses, respectively. One of the most practical methods is WQI, which indicates the degree of its suitability for various uses, including drinking. This index is usually obtained from the values ​​of general water parameters including dissolved oxygen, acidity, hardness, soluble solids, temperature, turbidity, nitrate, nitrite, and some basic ions. This index combines different water quality parameters to provide a numerical value that can be used for spatial comparisons. Besides, Wilcox's classification is one of the most important index in the field of determining the quality of agricultural water, which is calculated based on two parameters of electrical conductivity (EC) and sodium absorption ratio (SAR) as the risk of alkalinity.

The lowest and highest WQI levels were 30.1 and 674.0, respectively in the period 2010-2017. WQI time changes during the study period showed that the WQI is not significantly different between the first (2010-2013) and the second (2014-2017) periods. According to this index, in the first period, 7.9 % of the studied wells were in good condition, 5.6 % in bad condition, 13.5 % in very bad condition, and 73 % undrinkable in terms of drinking water quality. In the second period, 6.7 % of the studied wells were in good condition, 6.3 % in bad condition, 13.3 % in very bad condition, and 73.3 % undrinkable in terms of drinking water quality. In both time periods, the quality of none of the wells in terms of drinking was not in the high class. The results of descriptive statistics showed that the mean parameters of SO42-, Cl-, EC, TH, TDS, and HCO3- were two intervals higher than the standard. According to the results of the Pearson correlation test, there is a negative significant relationship between the pH and other parameters. The pH has the most negative significant relationship with TH. TDS and EC variables have the highest positive and significant relationship. This lack of impact can be attributed to the range of very low pH changes in groundwater samples in the two periods. There is also a significant negative relationship between pH and other parameters. The pH has the most negative and significant relationship with potassium. Moreover, according to the Wilcox index, in the period of 2010-2013, 87 % of the studied stations are in the S4C2, S3C3, S3C4, and S4C4 classes, which are unsuitable for agriculture and 12 % were in the C3S3 class, which by applying the necessary measures can be used for agriculture. Likewise, in the period 2014-2017, 91 % of the sampled stations are located in very salty and unsuitable for agriculture, and 8 % of wells. It is possible that the high extraction of underground water and the drop in the water level in the Zahedan region have caused the EC of water to increase. Also, drought can play a role as an aggravating factor in the change in water quality. The results of the Pearson correlation test showed that there is a significant negative relationship between pH and other parameters. Since in the comparison of various interpolation methods based on RMS and R2 for the WQI, the zoning map was drawn based on the kriging method due to its lowest RMS and highest R2.

Due to the high salinity of water, the study wells are not used for drinking and agriculture purposes but they can be used in ordinary applications such as washing. Besides, considering the quality characteristics of groundwater in the Zahedan region, it can be acknowledged that proper water management in this region is very important.

In recent years, the accumulation of heavy metals in the environment mostly due to its adverse impact on human health and wildlife has become a main concern (Al-Yemni et al., 2011). Meanwhile, sediments are the main storing heavy metals accumulations and serve as tools for assessing the status of river pollution (Taghinia Hejabi et al., 2010). At the same time, to using geographic information systems and Geostatistic methods is desirable. Many studies have been conducted by researchers such as Mortazavi and Saberi nasab (2017) in Meiqan, and Islam et al. (2015) in the water and sediments of the Korotoa River in Bangladesh. The purpose of this study was to investigate the concentration of lead, zinc, copper and chrome metals in surface sediments of GharehSo River and zoning of sediments of the region with Potential Ecological Risk Index and Muller's Geo-accumulation Index.

Assessment of groundwater quality and quantity variation in drought and semi-drought region are of most interest especially in Iran. This research is going to analysis salinity reasons by ground water quality mapping in the Khafr plain.

To do this the principal components of water quality extracted in spring and autumn seasons. Then Spline, IDW and Kriging interpolation techniques were fitted and evaluated for mapping. Finally, the best model was determined and ground water quality mapped using principle components.

Kriging recognized as the best model for autumn. The first component with high coefficients on TH, K, Mg, Ca, SO4, TDS, EC showed maximum values in the center and eastern south of the region. For the second component (SAR, Cl, Na) maximum values observed at the eastern south as well. The third component (PH) increased from center to the north and western north and decreased from center to the south and eastern south, and for the Forth (HCO3) maximum observed at the north and minimum in the eastern south. Spline method recognized as the best model in spring season. The first component (SAR, Cl, Na) showed maximum values in the eastern south of the region. The second (Th, Mg, Ca) maximum values observed at the center and the third component (PH) maximums in the west. Forth component (HCO3) maximum valued observed in the center and western north. Finally, the reasons of spatial and temporal variation of the components analyzed.

Due to the lack of facilities and commitment in climate stations establishment in large scale interpolation has become an important technique in spatial mapping studies. In this study, the Kriging interpolation technique as a prevalent technique has been explained and 17 Kriging models were evaluated, in order to select the most suitable one, using a case study. Because of increasing trend of drought in Iran and management necessity of water resources, meteorological drought of Fars province has been investigated in this study. For this purpose the calculated SPI-3 months of the Fars province synoptic stations were calculated. SPI-3 months is widely applied in agricultural drought studies. The empirical semi-variogram of the SPI-3 of the stations was calculated and graphed, fitting 17 Kriging models to select the best one using the cross-validation technique. Results showed that the Linear, Power, Square root, Logarithmic and Gaussian models were the best interpolation techniques for SPI-3 spatial mapping. Geostatistic methods could be used to determine the adequacy of the rainfall gauge stations, especially in the western mountainous region of the province. A complete-length of data could modify the results. The Gaussian model as one of the best-fitted models defines good spatial continuous of precipitation in the region. The semivariograms were fitted to data with the hypothesis of no precipitation trend. Considering the linear and non-linear trends of data, and analyzing error maps may result in better models.

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.

Groundwater resources are one of the most valuable national resources, and the protection of its quality is vital. One of the best ways to prevent groundwater pollution is to study the spatial changes in their quality and manage the utilization of water resources and land use. In terms of accuracy and effect of interpolation and origin, this study analyzes a comparison between OK, RBF and IDW interpolation methods for Pb, Zn and Cd . Also, the difference between the accuracy and uncertainty of interpolation and the source of pollution was analyzed and the results show that based on the spatial distribution of elements in the most polluted area in the southeast of the area around the coal and marble mine in the conservation area. Has been blacked out. Pollution has decreased from the south to the north of the region and the lowest amount of pollution is in the north of the region and near Bojagh National Park and Caspian Sea. According to WHO standards, it can be seen that for Zn, the whole area is within the WHO allowable range, the standard limit is 15 mg / L and the maximum Zn in the area is 1.64 mg / L. For Pb only small areas in the north of the area are within the allowable range. For Cd, the permissible limit of the global standard is 0.003 mg / L, which due to its spatial distribution, the whole area is contaminated with this element. Higher uncertainty of the elements is primarily distributed around the mines, which is related to the spatial diversity of toxic elements caused by human intervention. In addition to mining activities, the entry of industrial and municipal wastewater and effluents into groundwater resources can be considered as the cause of water pollution in the study area.

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.

Improper quality water use causes problems such as soil salinity and reduced soil permeability. In this study, groundwater quality in Sirjan city has been investigated using common diagrams and parameters in water quality classification for agricultural and industrial uses. Archmap and mini-tab software has been used to graphically display changes in quality parameters in the region and statistical classification of data. The results of the study showed that well (W2) located in the northeast of the aquifer has a higher quality than other sample wells, which is in the range of good to acceptable according to Schuler diagram and in good range according to Wilcox diagram. The amount of magnesium, calcium, sulfate, chlorine and sodium in the well was less than the World Health Organization standard. Also according to the obtained results of well (W12), (W13), (W15), (W17), (W3), (W9) (W10 (W14), (W3) which are located in the northwest, west, south and southwest of the aquifer, both in terms of drinking and agricultural use, of poorer quality than other wells. Enjoys. According to the classified maps, for effective parameters in the Schuler method, the parts of spatial continuity of the data were examined and after preparing the map, locations with different qualities were identified. Also, according to the results obtained from the classification map of the EC parameter by Wilcox method, a large part of the aquifer is in the inappropriate class and only parts of the northeast, east and middle parts of the aquifer are in the middle class.

Wind erosion is one of the environmental problems worldwide, particularly in arid and semi-arid areas of Iran. Different methods and models have been proposed to measure and monitor wind erosion in the recent years. One of the accurate models for measuring f wind erosion is the USEPA model. The purpose of this study was to evaluate the quantification of wind erosion with the USEPA model and the comparison of different interpolation methods for drowsing high-precision soil erosion mapping. For this purpose, 50 samples from 0-30 depth were taken from the study area. Based on the analysis of the physical properties of the soil, including the distribution of the primary and secondary particle sizes, climatic parameters such as evaporation and transpiration, rainfall, wind speed and also, the vegetation and topography characteristics of the area, the erosion rates of Q, Q30 and Q50 were measured. Interpolation methods including general kriging, IDW, LPI and RBF were compared. The results showed that the highest erosion emission rate of Q50 was 39 ton ha-1. The highest and lowest erosion rates for the Q30 index were 0.060 and 2.694 ton ha-1, respectively; for the Q index, the highest and lowest erosion rates were 0.009 and 0.055 ton ha-1, respectively. The results also showed that the IDW method for the Q50 index with the minimum error rate (RMSE) values of 3.94 and the mean absolute error (MAE) with the valueof 1.89 had the best performance among the studied models. The LPI model Q had the best performance with the lowest error (0.0086) and absolute absolute error (0.0021).

The goal of this research is assessment spatial and temporal distribution of groundwater quality in Sirjan plain using Arc GIS software. For this purpose, groundwater quality data were collected from Kerman province of water and wastewater company from1381-1397 years. Simple Kriging, ordinary Kriging and IDW method were used to predict spatial distribution of groundwater quality parameters (12 parameters). The variogram of each parameter was drawn using GS+ software for fitting the best interpolation model. Then, spatial distribution maps of groundwater quality were drawn using Arc GIS. Results showed that ordinary Kriging was the best interpolation method for most of the parameters and spherical and exponential models were the best variogram model. According to spatial distribution maps, concentrations of ions were increasing during the years studied and pollution was aggregated in west and south parts of Sirjan plain which agreed with well locations and using groundwater for agriculture in those zones.

In most of hydrometeorological studies and water resources management, flood and drought forecasting, irrigation planning and climate change studies, access to rainfall data and especially its spatial distribution (precipitation map), are of particular importance. Different models are used for spatial interpolation of rainfall data that generally fall into two categories of statistical and geostatistical methods. In statistical methods, interpolation is based on linear and nonlinear regression between main variable and covariate, but in geostatistical methods such as Kriging, spatial correlation of the sampled points is take in to account. There are many problems with spatial estimation of rainfall data especially in complex topography. Using correlated covariates is one of the answers to overcoming this problem. The altitudinal range of Mazandaran province fluctuates between -61 to 5610 meters, which creates various climates in this province. Besides, presence of the Caspian Sea in the north and Alborz Mountains Range in the south of the province make further complicates in spatial rainfall estimation, especially in the impassable heights of the province, which are a major water resource for large rivers. Since the elevated meteorological station in Mazandaran province is located on 2134 m, no rainfall data is available in area between 2134 to 5610 meters. So, this study was aimed to compare four interpolation methods include inverse distance weighting, Kriging, Co-kriging, and three-dimensional linear gradient. Also in this study, the role of covariates in precipitation estimation was investigated. Spatial data interpolation methods are used to estimate a variable at a particular point from actual data measured at adjacent points. The Inverse Distance Weighting method obtains the unknown quantity and performs the interpolation, by weighting the data around the estimated point. The interpolation methods use a set of points with known values around points with unknown data to estimate their values. This method is based on a geographical law that each phenomenon is related to other phenomena, but more depended to the close phenomena. Kriging as an advanced interpolation method is suitable for data with locally defined trends. This method can interpolate with the least variance of estimation that its error rate depends on the variogram specification. Co-Kriging is a suitable method when a covariate is present in all parts of network. In 3D Linear Gradient method, it is assumed that there is a linear trend along the length, width, and height of the region. In this study, in order to evaluate different interpolation methods of rainfall (Inverse Distance Weighting, Kriging, Co-Kriging, 3D linear gradient) the data from 25 synoptic and rain gauge stations were used in Mazandaran province. Surveying the statistical period of stations (1991-1991), the data of 2012 used to select the best interpolation method. Since the height of stations in Mazandaran province varies between -2120 and -21 m, precipitation data is not available for altitudes above 2200 meters. Therefore, altitude variable was used as an auxiliary variable in this study to obtain the most accurate estimation of altitude rainfall. In this study GS + and Mini tab software was used to calculate the estimated values of the models, Arc GIS software to prepare maps and Excel software for other calculations. Root Mean Square Error and Mean Bias Error were used to select the best interpolation method in this study. In variographic analysis, 5 types of semivariable models (Spherical, Gaussian, exponential, Linear and Linear to Sill models) were fitted to the data. The coefficient of determination and the ratio of structural changes to total variations were used to select the best half-variance. The best-chosen model has closer amounts of the coefficient and ratio to the number one. P-value and correlation coefficient were used to select the best covariate. Due to the importance of auxiliary variables in spatializing precipitation data, the variables of latitude, longitude, and altitude were used for the three-dimensional gradient equation. In order to better understanding of the studied methods, the map of annual precipitation changes in the province was plotted with different methods; then comparing them, the best rainfall map was selected. In this study, in order to determine the best interpolation method for monthly and annual precipitation data of Mazandaran province, four interpolation methods (Inverse Distance Weighting, Kriging, Co-kriging and three-dimensional linear gradients) were compared. Examination of Root Mean Square Error and Mean Bias Error revealed that the best interpolation method for long-term monthly precipitation was the 3D linear gradient method. But, the problem with this method and the other investigated methods in this study was overestimation of precipitation in high and low estimation stations in coastal and plain areas of the province. The overestimation was occured due to the lack of the number of station above altitudes of 2000 m in Mazandaran province. Therefore, the estimation of precipitation in the province's highlands had error. The outcomes of the best semicircle model in this study showed that the best models (except for July with low coefficient of determination) were spherical and exponential models. The results also showed that in hot months the spatial structure of precipitation data became weaker. Also, the impact of precipitation data in this province is about 30 km. The outcomes of correlation between monthly and annual precipitation data with latitude, longitude and altitude revealed that the altitude parameter had a significant correlation with other auxiliary parameters (latitude and longitude), in all months except July. Also, the correlation of latitude and longitude variables with precipitation was significant in some months. Therefore, it can be concluded that altitude parameter was the best auxiliary variable among the others to estimate monthly and annual rainfall in Mazandaran province. Distribution graph of annual rainfall variations with altitude along with regression equation indicated relatively good fit of linear equation to altitude rainfall fluctuations. Based on the results, the importance of the role of latitude and longitude variables for spatial precipitation data was determined. Therefore, latitude, longitude, and altitude variables were used for the three-dimensional gradient equation. Survey of annual precipitation maps showed that the three-dimensional linear, Co-Kriging and gradient methods had the most reasonable estimation of the spatial variability of precipitation in the province. According to the rainfall-altitude diagram, the average rainfall in the province is reduced to 500 mm of annual rainfall per 1000 meters. From the annual precipitation map survey with the selected method, it can be seen that only the western coasts of the province experience more than 1000 mm of rainfall per year. The slope of rainfall variations with altitude in the west of the province is more than east and due to the complex topography of the west of the province the west coast has more rainfall than the western altitudes.

Groundwater resources provide a large portion of the water used to irrigate farming lands. Therefore, one should adopt appropriate management strategies for utilizing these resources. This research investigates, both qualitatively and quantitatively, an acquirer using IDW interpolation technique, as implemented in ArcGIS 10.2 Software, so as to evaluate consequences of unleashed withdrawal of groundwater across Sarayan Plain. To this end, a total of 30 wellbores were studied and water withdrawal data during 2012-2016, water table level drop data during 1986-2017, and qualitative data during 2008-2016 were considered. Results of the present research indicated that, the rate of withdrawal from the wells in 2012 was larger than that in 2014 and minimal in 2016 when most of the wells within the studied watershed had their water withdrawal rates decreased from 24 L/s to as low as 9 L/s. Moreover, during the 31-year period studied herein, the water table level was found to go deeper by 20 m. The results further indicated that, during the studied period, the quality of groundwater across the plain decreased significantly, leading to degraded yield of the corps across the region.

Determination of water quality is an essential issue in water resources management and its monitoring and zoning should be considered as an important principle in planning. In this study, in order to investigate the quality of groundwater resources (springs, wells and qanats) in Semnan watershed, first, the water quality index for drinking and agricultural purposes was obtained by means of measuring SO4, Cl, Na, Mg, PH, EC, SAR, TDS in 55 groundwater sources. For calculating the parameters weight in WQI, the fuzzy hierarchy analysis process was used with the Chang's development analysis. Due to the lack of sampling points for zoning of the entire area, regarding the existence of EC data for the majority of groundwater resources used in this catchment (354 sources), as well as the high correlation (Adjusted R2=0.99) between WQI with EC, the mentioned indexes of other resources were estimated based on the regression relationship with EC. To analyze the spatial distribution and monitor the zoning of the groundwater quality, the ArcGIS version 10.3 and Geostatistical method such as simple Kriging and ordinary Kriging were used; additionally certain methods including Inverse distance weighting and Radial Basis Function were utilized. The performance criteria for evaluating the used methods including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), %RMSE and R2 were used to select the appropriate method. Our results showed that the ordinary Kriging and Radial Basis Function were the best methods to estimate the groundwater quality.

Groundwater as one of valuable water resources has always been of interest to researchers. Preparing an interpolated water level zoning map is one of fields that is can be acquired via best interpolation methods among different available methods. The Kriging method, based on semivariogram analysis, is one of traditional geostatistical methods. Interpolation precision is depended on the suitable selection of variogram. Empirical Bayesian Kriging (EBK) method is developed to estimated semivariogram parameters during simulation process. The objective of this study is investigation of EBK to increase the precision of groundwater level interpolation zoning and its results compare to other deterministic interpolation method including inverse distance weighted, radial basis function and local and global polynomial functions.
Material and This study is based on annual mean groundwater level of 57 deep well in Ghareh-Sou aquifer located in Golestan province during 2005-2016 period. Different deterministic and geostatistical interpolation methods evaluated using cross validation technique to groundwater level zoning. The best semivariogram selected for Kriging and EBK methods and finally the model with minimum error is determined and its map is drawn.
Findings: The results of cross validation in study area showed better results for degree two local polynomial methods among deterministic method even it had less error in comparison with Kriging method. The EBK methods, with simulation of fitness of suitable variogram on groundwater level data, led to decrease in Kriging error (23 to 16 meter) and had close precision to local polynomial method.
Although the error of prepared maps based on EBK and local polynomial methods have not significant differences, but there are considerable discrepancies between these maps. The EBK basis map show smoother spectrum of groundwater level changes and the drawn pattern is proportionate with general slope of study area.