فصلنامه پژوهش های فرسایش محیطی
سال سیزدهم شماره 4 (پیاپی 52، زمستان 1402)

Today, even the simplest human activities, such as walking, can have destructive consequences. Human movement on the earth may be done for economic, tourism, military purposes, etc. However, these movements lead to the trampling of the land and have consequences such as changes in the abundance and richness of vegetation, increase in runoff and soil density, and changes in erosion. In addition, the surface of the landforms is disturbed and with a sharp decrease in the threshold of shear speed, their vulnerability to wind erosion increases dramatically. Determining the sensitivity of landforms requires accurate tools, time and a lot of money, while the use of photography in monitoring the amount of changes in landforms can save time and money. The research that has been done so far in relation to trampling is generally based on two approaches. The first approach is the experimental method, that is, small undisturbed parts are selected in the study area and trampled according to the requirements of the experiment. The second method involves making long-term observations of the tracks that have been trodden. Chale Masileh is a part of the desert areas of the country, which is trampled by humans for various reasons. Economic activities such as harvesting potassium, magnesium and other materials from the bed of the salt lake, tourist areas such as Maranjab in the south of the region and military activities and holding large military maneuvers are among them. However, there is still no detailed information about the areas sensitive to trampling. Therefore, the current research tries to investigate the sensitivity of different desert landforms to trampling using a low-cost and fast method because in order to use and manage desert areas, it is necessary to understand the sensitivity of landforms.

The studied area mainly includes Mesila pit. The geographic coordinates of the salt lake as an index point in this hole are 30°34° north latitude and 50°51° east longitude. In this research, various materials, data and tools have been used in different stages of the research. The geomorphological map of the study area, and field data were collected in field operations using camera, tripod, GPS and plot. ArcMap, SNAP software were used to prepare the data, and Python 3.10 programming environment was used to run the model and draw graphs and outputs. 13 landforms were selected in different positions of the region. In the next step, an undisturbed area in the landform was selected and photographed, then trampled with 25 passes and a second photograph was taken. After recording the images by entering fourteen features for each pixel, the data was prepared to participate/to be applied in the model implementation. In the implementation of the model, 75% of the pixels were used as training data and the remaining 25% as test data. The ratio of changed pixels to unchanged pixels was considered as the change rate.

Evaluation of model efficiency using model relative performance characteristic curves (ROC) and area under the curve (AUC) shows that the values of 0.99 to 0.88 indicate the very good efficiency of the model in all samples. The RMSE value for the samples shows that in all the samples the mean square error value is less than 0.5, which confirms the good accuracy of the model in predicting the changed pixels. In addition, the average percentage of accuracy for samples using -k10 shows that the accuracy of the model in each sample is more than 80%. The importance of the image texture features in predicting the changed values in the photographed samples shows that in most samples the feature of the local standard deviation of the image of dissimilarity was the most important factor

Determining the sensitivity of the landforms of desert areas to human trampling is important for the management of these areas. To achieve this purpose, it is very useful to use low-cost, fast and accurate methods such as taking pictures and using machine learning algorithms. This study shows that the XGboost model and the photography method can measure the amount of landform changes with an accuracy of over 90%. These changes are determined according to the texture of the images and do not measure changes in height or soil density. However, it determines the number of changed pixels even when it is barely visible to the eye. On the other hand, the results of this method are the result of the influence of all effective variables such as slope, humidity, roughness, etc. While changes in altitude or density can be affected by one of the mentioned factors. The changes in different landforms based on the results of the model show that the least change is in the plowed land, which consists of very hard mud and salt. The surface of this landform is naturally very messy and uneven. The changes in the comparison of the two photos before and after trampling in this sample are hardly visible. The changed areas mostly correspond to the microslopes and small peaks that have been subjected to the most pressure in each pass. The amount of changes in the landform of sandy surfaces with a small amount of vegetation is 85%, which shows the most/highest changes among the samples. The key feature of this landform is the separation of land surface materials and vegetation.

  Land use and land cover (LULC) is a complex set of changes caused by the interaction of the natural environment and human activities, which has an important impact on the global environmental changes and sustainable development (Li et al., 2020). Changes in land use can occur due to population growth and the development of regional activities (Prayitno et al., 2020). Many of the problems caused by this development can be soil erosion, soil degradation, and the reduction of forest areas and biodiversity (Hu et al., 2019), which have had a major impact on the regional and global environment (Chen et al., 2020). Changes in LULC can not only directly affect the quantity and quality of land resources in human life, but also indirectly cause climate change, which is one of the important factors of global warming (Baoying et al., 2008), so it can change the hydrological regime and rainfall-runoff mechanisms of a region (Li et al., 2007). Factors such as land use changes, rainfall intensity, and degree of soil saturation, etc. cause the balance and natural flow of rivers to be disrupted (Ghanavati et al., 2014). The expansion of urbanization leads to an increase in impervious areas and a decrease in rainfall absorption in the watershed, causing changes in the river's hydrology, creating runoff after rainfall, and as a result, reducing the recharge of the aquifer (Quan et al., 2015). Around the world, flood includes almost one-third of natural hazards and harms people more than any other types of disasters (Asinya et al., 2021). Change detection of LULC is possible by comparing the changes that occurred in a certain area according to the images taken at different times. Today, satellite data on land resources are available and are relevant and useful for LULC studies (Shanmugapriya et al., 2016) because of having some features such as high temporal frequency, accessibility, showing global land cover for consecutive years, being suitable for calculations, and having a wide range of uses which make them have a high potential for analyzing spatial and temporal changes (Kantakumar, 2019). In recent years, due to climatic reasons, the occurrence of floods has increased in the world (Ghanavati et al, 2013). Golestan province is no exception to this rule. In the recent floods in Golestan, natural factors such as the wet winter have led to the wetting of the soil, the filling of storage channels, and the rise of the stagnation level, and as a result, the runoff coefficient has increased. In terms of human factors, it is possible to point out the impact of non-observance of the principles of land preparation and improper land use allocation, deforestation, encroachment on river boundaries, and insufficient dredging of the main channels especially the estuary, which have increased the possibility of occurrence of natural hazards. Hydrological models are the basis for understanding the cause-and-effect relationship between hydrological changes and land use changes (Shokouifar et al., 2022).

  In this research, Landsat TM, Landsat ETM+, and TIRS OLI satellite images from 1986, 2006, and 2020 have been respectively used to classify and investigate land use changes in the Gorgan River basin. ENVI 5.6 software was used for image processing and data analysis, and ArcGIS 10.7 software was used to obtain output from image processing. ENVI 5.6 software was used to classify the desired images using the Random Forest algorithm and using the EnMap-Box 2.2 plugin. TERRSET2020 software was used to model the changes. The Kappa coefficient was used to evaluate the accuracy and precision of the classification as well as to compare the classification result with the ground reality. For land cover classification, six land use classes, including forest, urban areas, agricultural lands, water areas, pastures with good vegetation cover, and land with poor cover (pasture and barren land) were considered. In this study, the effect of land use on runoff potential was also simulated through the semi-distributed SWAT model. Model implementation was done in Arc GIS 10.7 environment. After preparing the required maps and preparing the input data, three different SWAT models were designed for the Gorgan River watershed. The first model was used from 1985 to 1996 from the land use map of 1986, the second model was used from 1999 to 2009 from the land use map of 2006, and the third model was used from 2010 to 2020 from the land use map of 2020. In the first stage, by entering the Dem map and producing the flow network by the model itself, based on the threshold limit of 14,000 hectares as the minimum drainage level and entering the Agh Qala hydrometric station as the outlet of the basin, the Gorgan River watershed was divided into 32 sub-basins. After drawing the boundary of the basin, sub-basin, and flow network, the physical parameters related to the basin and each sub-basin, including area, length of the main waterway, slope, height characteristics, etc. are calculated. In the next step, the soil and land use maps must be entered into the model and the slope classes must be defined by the user, and by combining them, hydrological reaction units (HRU) are produced in each sub-basin. The number of HRUs can be changed multiple times for each land use, soil, and slope by determining a minimum percentage of the watershed area that is defined by the user. In the next step, climate data including daily precipitation and temperature information are entered into the models and the appropriate method for calculating potential evaporation and transpiration is determined based on the type of climate data available. In this study, the Hargreaves-Samani method was used to calculate potential evaporation and transpiration. The method of variable storage coefficient was used for trending the flow. Also, management information such as planting, fertilizing, irrigation time, and harvesting of the dominant crops of the basin were introduced to the models. In the final step, the model was run to simulate monthly runoff, considering 3 years of training for all three models.

The analysis of annual runoff in three scenarios shows that under the second and third scenarios, the surface runoff has increased by 20.47 and 46.45%, respectively, compared to the first scenario According to the investigations, it is clear that water efficiency has been increasing from 1986 to 2020. This increase can be attributed to land use changes, including the reduction of forest area and the increase of agricultural land, pastures, and residential areas. An increasing trend is observed in the northeast sub-basins compared to the southwest, which is due to the reduction of forest land and its conversion to agricultural land in the northeast. In 1986, the water yield of most sub-basins (45.98% of the basin area) is less than 194 mm. While the water yield in 2020 has increased by more than 290 mm in most of the basins (56% of the basin area) and in sub-basins 10 and 12, which are mainly degraded forest areas and are at a higher level up to 378mm, the variable of agricultural land with an average participation rate of 43.63% has had the highest change in runoff from 1986 to 2020. And after that, forest lands increased by 37.25% and played an important role in creating runoff in the Gorgan basin.

The results show that urban land has increased from 3.20% of the total land of the region in 1986 to 4.66% in 2006 and this number has reached 5.60% in 2020. According to the investigations carried out in this research, it can be concluded that the area of forest land decreased by 45.56% between 1986 and 2020,The largest increase in the area of built land occurred in the second half of the period between 2006 and 2020. However, the decrease in the area of forest land from 1986 to 2020 is very impressive. These changes show the process of destruction in the region by replacing these uses with pastures, barren lands, and forests. To evaluate the effect of land use change on runoff, three SWAT models were implemented using three land use maps for the study area. The simulation results of the flow in the region were acceptable in all three models, so the coefficient of explanation between the observed and simulated data showed acceptable results. After the SWAT model simulation, three optimal values of the parameters of each period were placed for the defined scenarios. The results showed that with the change in land use, the value of the curve number in the second and third scenarios increased by 0.79 and 1.50%, respectively, which was due to the increase of barren lands and the decrease of vegetation in the region. The annual study of runoff in three scenarios shows that in the second and third scenarios, surface runoff has increased by 20.47% and 46.45%, respectively, compared to the first scenario. According to the studies conducted on the impact of each land use in increasing the runoff, the highest impact related to agricultural lands has increased by 12.38% in 2020 to the amount of 43.63% compared to 1986. The subsequent use of forest lands with a decrease of 34.73% has caused an increase of 37.25% of runoff in 2020. As a result, the water output volume has increased by 6.89% of the basin. Also, the rate of evaporation and transpiration of the second and third scenarios was reduced by 2.07 and 7.59%, respectively, compared to the control scenario. The reason for this is the reduction of vegetation including water lands (gardens and agriculture) in the basin in the second and third scenarios.

Vegetation is one of the most important components of terrestrial ecosystems, which plays a vital role in carbon regulation and balancing, energy exchange and climate stability. Also, temperature and rainfall are the most important influential factors in changing the vegetation index, NDVI. Understanding the relationship between rainfall, NDVI and temperature is essential in forestry planning over each region. Accordingly, the main objectives of this research are 1) to monitor the annual changes of NDVI from 2001 to 2020 using linear regression’s slope and Sen’s slope estimator methods, and 2) to investigate and determine the relationship between NDVI and the climatic components, specifically rainfall and temperature, in Semnan province.

Semnan province was selected as the study area to evaluate the relationship between climatic components and vegetation cover by using remote sensing data. The NDVI data was extracted from the Terra MODIS product in a spatial resolution of 500 meters and was processed to evaluate vegetation changes in Semnan province during the years between 2001 and 2020 on monthly scale. After that, the monthly rainfall and temperature data were obtained from both synoptic and climatology stations; then they were converted into annual scale. Furthermore, the rainfall and temperature reanalysis grid-base data, ERA5-Land, was downloaded in about 9 km spatial resolution from 2001 to 2020. Reanalysis data usually contains systematic error compared to observational data, which can affect the output results and requires to be corrected. Consequently, we utilized one of the recent bias-correction approaches, the Quantile Mapping (QM) bias-correction method, to correct biases over the entire distribution of the rainfall and temperature reanalysis data. At this point, each set of the rainfall and temperature grid-based data was resampled to 500 meters based on the spatial resolution of NDVI pixels. Next, each series of rainfall and temperature data were corrected based on QM method from the years 2001 to 2020 according to the availability of the NDVI time series data. The relationship between annual rainfall and temperature with the NDVI was calculated in each month of the year (2001-2020). In this study, linear regression and the non-parametric method of the Sen’s slope estimator were used to investigate the changes in NDVI trend for each pixel from 2001 to 2020. Finally, to check the accuracy of the relationship between vegetation, temperature and rainfall, the coefficient of determination was used.

The linear regression’s slope indicated that 25% of ​​Semnan’s area had vegetation variations close to zero in each month during the years 2001 to 2020. It means that NDVI values did not change significantly and it was almost unchanged. Moreover, based on the Sen's slope estimator, the results showed that there was no noticeable change in decreasing or increasing the amount of vegetation in about 75% of ​​Semnan’s area. The analyses also showed that the coefficient of determination between NDVI and rainfall varied from 18% to 44% in different months, and the highest relationship values were observed in September and December. Moreover, in more than 50% of the Semnan’s area, the relationship between NDVI and rainfall has varied from zero to more than 42%. The results indicated that vegetation cover has no significant relationship with annual rainfall in both winter and spring. Furthermore, in 50% of the study area, the estimated NDVI variation indicated zero or negative values in each month, which confirms that the vegetation cover has not changed significantly or it has decreased slightly in response to the temperature.

In recent years, the evaluation of changes in NDVI time series has been developed by using satellite images and remote sensing techniques. In addition, the climate components like rainfall and temperature are among the factors affecting the growth of vegetation cover, which has attracted the attention of many researchers. By considering the linear regression’s slope and Sen’s slope estimator, the results showed that NDVI did not change significantly during the years 2001 to 2020 in Semnan province, but it decreased in some areas. Moreover, the effect of rainfall and temperature demonstrated that vegetation has either direct or indirect relationships with rainfall or temperature in some months (positive or negative values, respectively). The linear regression’ slope between annual temperature and NDVI showed that in 50% of ​​Semnan’s area, NDVI variation was estimated to be nearly zero or negative in each month. Generally, in arid regions like Semnan province, the growth of plants is controlled by two climatic factors, rainfall and temperature. In arid and semi-arid regions (where the amount of rainfall is low, such as Semnan province), or in regions where the percentage of humidity is high, the maximum relationship between NDVI and the rainfall was not observed.

In arid and semi-arid regions, due to the fragility of the ecosystem, the reduction of vegetation cover can have irreparable consequences such as increasing the movement of soil particles. This action will eventually lead to wind erosion and will increase dust in the region. Therefore, the present study was conducted to evaluate the monthly changes of NDVI from 2001 to 2020 by the linear regression’s slope and Sen’s slope methods. In addition, the relationship between NDVI and climatic components of rainfall and temperature were discussed. The findings showed that results of the linear regression’s slope and Sen's slope estimator are both almost the same. The relationship between rainfall and NDVI indicated that in 75% of Semnan’s area, the estimated value of coefficient of determination had the highest value in September and December. It means that with the increase of rainfall, vegetation cover also increases. Based on the linear regression’s slope between the annual temperature and NDVI, it was observed that in almost 50% of the study area, NDVI variations were approximately estimated to be zero or negative in each month, which means the vegetation cover has not significantly changed or even decreased by changing temperature. In future studies, it is suggested to use other remote sensing NDVI products such as Landsat satellite and even other climate reanalysis data to have a more accurate view of vegetation cover changes in the study area.

Landslide can be defined as mass movement of materials on sloping slopes under the influence of mass gravity and triggering factors such as earthquakes, floods and heavy rains. This phenomenon is one of the natural hazards that causes a lot of loss of life and money every year in mountainous, rainy and seismic areas. By zoning the risk of landslides, it is possible to identify sensitive areas with high potential for landslides and by providing solutions, appropriate control and conduting effective management methods, the occurrence of landslides can be partially prevented or the damages caused by them can be reduced.

In this research, the effect of the rainfall system of April 2018 on the occurrence of landslides in Boyer Ahmad and Dena counties in Kohgiluyeh and Boyer Ahmad province has been investigated. The zoning of landslide risk has been performed using Haeri-Sami experimental model and the integration of thematic rock maps based on geology, slope angle, length of fault, length of road and river, amount of rain, intensity of rain and earthquake have been done in the scale of 1:1500000. After preparing the landslide zoning map, by carefully examining the Google Earth images, the areas of the studied areas where the potential of landslides were expected to be higher were identified. Then, during the extensive field visits, the geographical coordinates of the observed landslides were recorded and by preparing the point coordinates of the landslides in the Arc Map software environment and transferring them to the Google Earth images, the location of the landslides was recorded. The identification and range of each location was prepared in the form of a polygon file and transferred to the Arc Map software environment, and in this way a digital map of the distribution of landslides in the studied area was prepared. The density ratio index (Dr) was used to evaluate the landslide risk zoning map.

The results showed that only in terms of the geological factor, about 23% of the area under study has a high sensitivity to the occurrence of landslides, while other factors including the slope angle (73%), fault length (99%), and length of roads and rivers (98%) have little sensitivity to the occurrence of landslides. In terms of the amount of rainfall, in the average rainfall mode (78% of the region) it has moderate sensitivity and in the mode of April 2018 rainfall, 82% of the region is of medium sensitivity and 10% of high sensitivity with regard to the occurrence of landslides. In terms of the intensity of rainfall (79%) and earthquakes, about 58% of the area under study has a medium sensitivity to the occurrence of landslides. Landslide risk in 4 classes: no risk, very low risk, low risk and medium risk, respectively, have an area distribution of 15.1, 36.07, 43.33 and 5.5% in the case of average rainfall and 22.54, 7.9, 38.03 and 31.53% in the case of April 2018 rainfall. In the average rainfall, about 94% of the studied area is in the classes without risk to low risk of landslides, and in the rainfall of April 2018, about 69% is in the low and medium risk range.

Based on the results of landslide risk zoning, it can be said that the occurrence of landslide risk in the study area based on the studied model and the mutual conditions of the effective parameters in the average rainfall is almost low and in the rainfall of April 2018, the risk is low to moderate. Therefore, it can be concluded that the rainfall in April 2018 has a relatively significant effect on the risk of landslides. In connection with the evaluation of the efficiency of the model using the density ratio index, the model has been able to separate the landslide risk zones well, and in the very low risk class, the amount of rainfall is more accurate in both cases.

Soil and air are two essential elements in the life of creatures on the earth. Their interaction in certain conditions can cause many risks; among these dangers, dust storms can be mentioned. Under the conditions of dust storms, a large amount of dust is emitted in the air and the horizontal visibility is reduced to less than 1000 meters. Therefore, due to the important role of the dust phenomenon, it is highly necessary to understand the spatial-temporal changes and to analyze their long-term variations. Jazmurian region, located in the southeast of Iran, between the two provinces of Kerman and Sistan and Baluchistan, has become completely dry and turned into a desert due to drought and construction of numerous dams, and has turned this region into one of the key areas of dust production in the country. Therefore, the purpose of this study is to investigate the temporal-spatial changes of dust storms in relation to climatic parameters in Jazmurian wetland, which is of particular importance in order to properly manage this area to face the problems caused by dust storms.

In order to carry out the present research, the meteorological data related to the synoptic stations located in the Jazmurian wetland area for a period of 20 years (2000-2020) were received from the Iranian Meteorological Organization. Climatic data used in this research include temperature, precipitation, relative humidity, wind speed and direction, horizontal visibility, and remote sensing data including Aerosol Optical Depth (AOD) and Normalized Difference Vegetation Index (NDVI). The research method uses a combination of statistical analysis, observation and remote sensing. In order to check the AOD and NDVI, the monthly data of MODIS sensor was used. In this study, 12 synoptic stations that had the longest and most complete statistical periods were used in order to identify the temporal-spatial changes of dust occurrence in Jazmurian wetlat. The Mann-Kendall test was used to examine the trend of time changes. The slope of the Sen estimator was used to check and confirm the accuracy of the trend changes. In order to better understand the spatial distribution pattern of dust events in the Jazmurian wetland basin, the inverse distance interpolation method (IDW) was used. Also, Pearson's spatial correlation analysis was used to investigate the mutual effects between the indicators.

The 20-year average review of the indicators showed that the maximum value of aerosol optical depth in the studied area was 0.3, which is seen in the central part of the wetland. The vegetation index also showed that the maximum value of this index was 0.2, which covers most of the northern, northwestern and western parts. Examining the average rainfall trend showed that the maximum and minimum rainfall in this period are about 219 and 85 mm, respectively. Meanwhile, the 20-year average temperature survey showed that the maximum and minimum temperatures were 28.7 and 17.2 degrees Celsius, respectively. The fact that the maximum rainfall is in the northern and western part is in harmony with the minimum temperatures in these areas. The examination of the average wind speed showed that the maximum speed was 3.6 m/s, which was mostly in the central, west and northwest, south and southwest parts. The results of the wind direction index showed that the lowest wind direction is in the northern parts of the region and the highest wind direction is in other parts of the Jazmurian wetland basin. The maximum humidity in the studied area is 43.73%, which is mostly seen in the north, northwest, west, south, and southwest parts.  Examination of the horizontal visibility index showed that the maximum amount of horizontal visibility was in the northeastern and northwestern parts of the region. Also, the process of changes in the Mann-Kendall test showed that the annual averages of the indicators of wind direction, wind speed and horizontal visibility have been increasing with a positive slope, and the trend of changes in the relative humidity index has a negative slope and indicates a decrease in the period of 20 years. The results of the correlation analysis (at a significance level of 5%) showed that the highest correlation of the optical depth index was with the wind direction parameter and the lowest correlation was with the relative humidity index.

The growing trend of the emission of dust particles caused by the phenomenon of wind erosion in recent decades has caused major concerns at different regional, national and global levels. Therefore, it is necessary to understand the temporal-spatial changes of the dust caused by these events in order to reduce their adverse consequences in different regions. Accordingly, in this research, with the help of this knowledge, information related to the optical depth of particles in the air (AOD) and the vegetation cover index (NDVI) were investigated using the MODIS sensor satellite data. Using the meteorological data received from the website of the National Meteorological Organization, the climatic data of rainfall, temperature, relative humidity, horizontal visibility, wind direction and wind speed were evaluated in the period of 2000-2020 and finally the correlation between the AOD index and other climate parameters was evaluated. The results of the optical depth investigation showed that the maximum optical depth is located in the central parts of Jazmurian wetland. The trend of changes in rainfall and temperature indicators also showed that the trend of these two parameters was increasing; this increase in temperature is more noticeable. Correlation investigation between optical depth and other parameters showed that the highest correlation of optical depth index was with the wind direction parameter and the lowest correlation was with the relative humidity index. In general, we can conclude that the optical depth of airborne particles is highly dependent on environmental factors, which is more evident in arid and semi-arid areas, especially in Iran and the Jazmurian wetland area. In recent years, the area of the wetland has acted as a source of dust. Therefore, by using the remote sensing data obtained from the MODIS sensor and the climate data, it is possible to examine and analyze the trend of dust changes in the units of time and space.

Soil erosion has been increasing in Iran in the last decade due to the lack of optimal use of pasture and forest lands. This factor destroys the ecological conditions suitable for the life of organisms while wasting the soil and producing sediment. Preservation and development of vegetation in pastures, such as medicinal plants, plays a significant role in preventing soil erosion. Salvia macrosiphon Boiss is a perennial species and has an essential oil plant of the Lamiaceae family, which is traditionally used in the treatment of respiratory diseases, depression and urinary tracts. Considering that in recent years, due to the economic and export value of this species, its natural habitats have been overexploited, and also due to climate changes and extensive droughts of the past few years, as well as harvesting in this way, the process of soil erosion in its natural habitats has been accelerated. Therefore, the present study was conducted with the aim of investigating 19 ecological attributes of the main habitats of S. macrosiphon in Fars and Hormozgan provinces.

In the current study, the natural habitats of this species were determined by using Flora Iranica and with the assistance of Hormozgan Agricultural Research, Education and Extension Organization experts. Characteristics of five habitats in Fars province (including Kazeroon, Farashband, Dehram, Evaz and Jahrom) and three habitats in Hormozgan province (including ZahedMahmoud, Bekhan and Sirmand) were studied. The climatic features of each natural habitat such as latitude and longitude, altitude, mean annual temperature, minimum and maximum temperature, and mean annual precipitation were recorded. From each habitat, three soil samples were taken from a depth of 0-30 cm. The percentages of clay, silt, sand, pH, electrical conductivity (EC), organic carbon, absorbable phosphorus, absorbable potassium, total nitrogen, calcium carbonate equivalent (CCE), absorbable iron, absorbable zinc, absorbable manganese and absorbable copper were measured. To analyze the studied environmental factors and the measured soil parameters, the multivariate analysis method including Pearson correlation coefficient of traits, decomposition into principal components and cluster analysis was used by SPSS ver. 26 software.

The results revealed that S. macrosiphon was distributed at an altitude of 400 to 1550 meters above sea level in Kazeroon, Farashband, Dehram, Evaz, Jahrom, ZahedMahmoud, Bekhan and Sirmand natural habitats and on the slope between 0 to 20%. The maximum and minimum annual rainfall were recorded as 156.2 and 387.7 mm, respectively. The average annual temperature was recorded at 24.5 °C and also, the minimum and maximum temperatures were -3.8 and +49.5, respectively. This species grows in the loam, sandy loam and silt loam soil textures with a pH of 7.7-8.4, an EC of 0.47-6.87 dS/m. Furthermore, it was found that S. macrosiphon was spread in non-saline and saline soils (electrical conductivity 0.47 to 6.87 dS/m). The soil in which the species was grown in the studied areas was poor in terms of available potassium content (46 to 302 ppm). The highest concentration of absorbable Fe (6.7 mg/kg) in Farashband habitat, absorbable Zn (3.8 mg/kg) in Kazeroon habitat, absorbable Mn (0.28 mg/kg) in Kazeroon habitat and absorbable Cu (0.2 mg/kg) was observed in Zahedmahmoud habitat. On the other hand, the lowest concentration of absorbable Fe (1.5 mg/kg), absorbable Zn (0.6 mg/kg) and absorbable Mn (3.3 mg/kg) was observed in Dehram habitat. Also, the lowest amount of absorbable Cu (0.3 mg/kg) was obtained in the two habitats of Dehram and Evaz. Using cluster analysis, habitats were divided into four clusters according to which habitats with common characteristics were placed in the same group.

The findings of this study showed that S. macrosiphon is resistant to hot and dry conditions. The two habitats of Dehram and Zahedmahmoud are prone to flooding due to the high percentage of silt in the soil texture. A high percentage of silt increases soil runoff during rainfall because the silt texture of the soil is one of the factors that prevents water from penetrating the soil. If the percentage of silt in the soil texture is high, the soil can absorb less water, and as a result, more water flows during rainfall, which can lead to soil erosion. The lack of absorbable phosphorus and potassium, as well as the lack of nitrogen and organic carbon, are the most obvious attributes of the soil of the S. macrosiphon habitats, which may cause limitations in its growth. The parent material of the soils of the studied areas originates from calcite limestone. In areas where the soil is calcareous, the temperature and pH are high. Based on the estimated Pearson correlation coefficient between the traits, it was determined that the higher organic carbon soil will have the higher amount of nitrogen. The presence of organic matter and the amount of nutrients have a direct relationship. This means that wherever the amount of organic matter is higher, the amount of nutrients is also higher. On the other hand, an increase in temperature leads to a decrease in soil organic matter. The results of analysis into the main components led to the extraction of five factors, which, according to the effectiveness of the variables, the selection based on the first component will lead to habitats with heavier soil texture and higher potassium concentration, which can provide a possibility of pasture development by this species in soils with a higher clay content and wide range of temperature fluctuations. In general, this research demonstrates considering the importance of S. macrosiphon in terms of being an under-expected pasture and medicinal species, as well as taking into account limitations such as low rainfall, and irregular spatial and temporal changes. Due to (limited) rainfall, high evaporation and transpiration and unprincipled exploitation, the condition of the habitats is extremely fragile and will lead to the instability of these ecosystems. By planning and conducting optimal management of pasture and medicinal resources through the obtained information, it is possible to domesticate and cultivate this plant by following the microclimatic conditions of this plant and providing natural conditions for its growth and protecting its valuable genetic resources. After all, these studies help us to be able to use optimal methods to improve and preserve the soil and prevent its erosion through plant preservation.

Conservation agriculture is considered by many researchers as a sustainable agriculture strategy. In this type of agriculture, continuous minimum mechanical soil disturbance (no tillage or reduced tillage), permanent organic soil cover (residues or cover crops) and diversification of crop species grown in sequences and/or associations are important components. This 11-year long-term (longitudinal) study was conducted to investigate and quantify individual and combined effects of conservation agriculture measurements (namely, plant rotation, crop residue retention, and conservation tillage) on soil volumetric water content (VWC) and soil bulk density (BD) of two sandy loam soils in a temperate region, Denmark. The possible effects of different sampling seasons (autumn and spring) on the study results are also investigated. It was hypothesized that there would be a positive effect of residue retention and diverse rotation on especially the no-tillage treatment.

In a randomized complete block experimental design with four replications, an 11-year experiment was conducted in two research areas of Denmark. Three crop rotations/residue management treatments were compared. Tillage was included as a split plot factor. The rotation R2 is a rotation of winter crops (mainly cereals) with the retention of plant residues, the rotation R3 is a mixture of winter and spring plants (mainly cereals) with the removal of residues, and the rotation R4 is the same mixture of plants similar to R3 with the retention of residues. Each rotation includes traditional plowing treatments to a depth of 20 cm (MP), harrowing to a depth of 8-10 cm (H) and direct drilling (D). In mid-autumn of 2013, and early spring of 2014, soil samples were taken by cylinder from the depths of 4-8 and 12-16 cm (in 2014 only 4-8 cm depth were sampled). The amount of VWC, in matric potentials from 0 to 100 kilopascals, as well as the apparent soil bulk density were measured. Also, the possible effects of different sampling seasons (autumn and spring) on the study results are investigated.

Tillage system and residue management, significantly affected soil properties studied. Direct drilling significantly increased bulk density at both depths (1.31 and 1.38 gr.cm-3). The amount of VWC of the soil was also significantly different in tillage and plant residue treatments. At the depth of 4-8 cm, minimum tillage treatments (D and H) retained more moisture compared to MP (38.6-23% vs 35.8-22%). This trend was different in the depth of 12-16 cm and MP treatment showed the highest VWC in all suctions. The treatment of plant residue retention (R4) also caused the lowest amount of BD, especially at the depth of 12-16 cm. This treatment also significantly increased the VWC of the soil in macroporosity at both depths. The interaction effect of tillage and residue management showed a trend of lower BD where D and MP were combined with residue retention (R4) compared to their combination with residue removal (R3). The positive effect of retaining plant residues on increasing the water holding capacity in soil in a range of moistures that are available for plants was shown only in the spring sampling (2014) in both investigated areas. In the autumn sampling (2013), this effect was observed only at Foulum area and in low suctions (macroporosity).

The above observations seem reasonable considering the depth of soil that is disturbed in different tillage systems of this study. In the H treatment, which creates the lowest bulk density at Foulum area, at a depth of 4-8 cm and has a lower bulk density, plowing (without turning) is done with a disk to a depth of 8-10 cm. It is obvious that the depth of 4-8 cm is located in this depth range and the reduction of bulk density is expected. In treatment D, where minimum soil disturbance occurs at this depth, higher bulk density is expected. In the lower soil depth, i.e. 12-16 cm, MP has resulted in the lowest soil bulk density compared to the two minimum tillage systems, D and H. The cause may be attributed to the plowing and soil inversion at a depth of 20 cm in this treatment. These observations are consistent with the results of several other studies (Ball et al., 1994; Bescansa et al., 2006; Hill et al., 1985; Schjønning & Rasmussen, 2000). In this regard, Tollner et al. (1984) studied the effect of no tillage and plowing with a mouldboard on the soil bulk density and reported that at a depth of 15 to 25 cm, bulk density in the no tillage system was higher than that of the mouldboard plowing, while at a depth of 30 to 40 cm, bulk density of the soil was higher in the mouldboard plowing system than no tillage system.The positive effect of plant residues on reducing the soil bulk density after 11 years of implementing the treatments corresponds with the results presented by Blanco-Canqui and Lal (2007) and Lal (2000). They reported a significant increase in soil porosity after 10 years of application of 8 and 16 tons/ha per year of wheat and rice residues to topsoil. However, at zero kPa suction (saturation) and 1 kPa suction, the R4 treatment shows an increasing trend in the amount of water retention in the soil at both investigated depths. This means that plant residue management has increased the amount of moisture that can be stored in macropores. The results of this study are consistent with the results obtained previously from the same long-term test, especially at lower suctions (Abdollahi et al., 2014). Maintaining soil moisture in low suctions (macroporosity) in spring sampling (2014) at both sites and in autumn 2013 at Foulum is not very effective in terms of meeting plant needs. However, it is a desirable feature from the point of view of reducing the amount of runoff production and water erosion.

The change and evolution of land and water erosion zones through time shows the stability of natural lake ecosystems. Therefore, monitoring the fluctuations of the lake boundary through the preparation of remote sensing data through time, with high spatial and temporal resolution, can reveal the effects of natural hazards, including drought. Compared to other methods, the remote sensing methods due to their ability to easily and cheaply access data, their high accuracy and comprehensiveness, wideness of satellite images and their spectral diversity are considered an effective tool in water resource management and lakes monitoring. Therefore, the importance of monitoring the changes in water-land erosion areas of Lake Neor can provide a suitable platform for making efficient management decisions and prioritizing executive programs to deal with the water shortage crisis. This research has been conducted with the aim of evaluating the changes in water and land erosion areas of lake Neor (as the largest fresh water lake in Ardabil province and one of the most important tourist attractions of this province) in relation to climatic factors during a period of 32 years. This issue has been less discussed in the studies conducted in the region.

 Most of the data of this research are time series of Landsat 5 and 8 images during the period of 1988-2020, which were obtained from the website of the US Geological Survey. The map of the studied basin was extracted using ASTER DEM and ARC HYDRO tool. Atmospheric and geometric corrections on the images were applied in ENVI 5.3 software. Water areas were extracted using AWEI, NDWI and NDVI indexes. ARC GIS 10.2 software was used to get the final outputs of the maps. The 32-year climate statistics of the synoptic station of Ardabil city were obtained from the Meteorological Organization of the country in order to investigate the causes of changes in water erosion zones.

 The application of three different water indicators on the images indicated that the water area of the lake has decreased from 1988 to 2020 . The largest water area obtained from the AWEISH index in 1992 is 4.1 square kilometers and the smallest lake area in 2019 is 2.2 square kilometers and 2005 with 4.08 square kilometers. The lowest area of the lake according to this index is related to the year 2019 with an area of 2.28 square kilometers and in 1991 with an area equal to 2.26 square kilometers. These numbers have been repeated in other indices with a slight difference. This great fluctuation of the water level of the lake during three decades can have several reasons. In this study, rainfall changes during the studied years have been considered as the main cause. The comparison of the fluctuations of rainfall with changes in the lake level shows the water of the lake has prograded a lot towards the land in the rainy years (1992 and 2005). Also, the performances of three water indexes (general accuracy and Kappa coefficient) were evaluated in the form of Support Vector Machine (SVM) algorithm and the Minimum Distance method. The overall accuracy and Kappa coefficient in all three indicators is higher than 0.92 which indicates the used methods in the extraction of water have high compliance with the land use.

In this research, the surface dynamic changes of Lake Neor in relation to climate fluctuations (precipitation) during the period of 1988-2020 were studied using multi-temporal data. In order to detect the temporal-spatial changes of the lake water level, three different indices of quantitative water extraction (AWEISH, NDVI and NDWI) were used. The performance of each of them (general accuracy and Kappa coefficient) was evaluated in the form of Support Vector Machine (SVM) algorithm and the Minimum Distance method. It was found that the AWEISH index had the lowest error compared to other indices used in the detection of water areas. The results of monitoring three decades of changes in the area revealed that the annual average of the water area of the lake has experienced a decreasing trend. This downward trend has become more intense during the last years of this period due to climate changes and occurrence of recent droughts. In order to optimally manage this lake, considering its environmental importance and tourist attractions, the boundaries of the lake's bed and privacy should be determined by the relevant organizations such as Department Of Environment, Water Resources Management Company and remote sensing researchers. Another suggestion in this regard is to use these indicators in places with diverse geomorphological, climatic and environmental conditions simultaneously to be able to have a better assessment of their accuracy in detecting water areas.

Carbon dioxide is one of the main greenhouse gases that affect the world's air temperature. Small changes in the amount of carbon dioxide emissions from the soil have a significant effect on the concentration of this gas in the atmosphere. Soil respiration, the process that emits carbon dioxide from the soil to the atmosphere, is one of the most important carbon flows in the ecosystem and includes two components of heterotrophic respiration (microbial respiration) and autotrophic respiration (root respiration). Researchers measure the rate of soil respiration for every 10 degrees Celsius of temperature change with an index called temperature sensitivity of soil respiration (Q10). The evidence shows that the Q10 value of the soil is not constant and has a negative correlation with temperature and a positive correlation with soil moisture. Also, the amount of soil organic carbon, incubation temperature and the interaction of these two have a significant effect on soil organic carbon decomposition. Accordingly, this research measures the temperature sensitivity (Q10) in soil under tea cultivation and investigates its relationship with some soil chemical characteristics and topographic indices.

After surveying the east and west tea gardens in Guilan province in the north of Iran, 200 samples were taken at a depth of 0 to 40 cm. The experiments were conducted to determine Organic Carbon, Labile carbon, Bulk density, PH, Cation Exchange Capacity, Microbial Biomass and soil microbial respiration. To measure Q10, two temperature treatments of 25 and 35 °C were used. Elevation, slope and aspect were obtained using a DEM map in ArcGIS 10.5 and other topographical indicators such as wetness index, slope length, relative slope position, catchment area, channel network base level, vertical distance to channel network, convergence index, profile curvature and plan curvature were extracted from DEM map in Saga GIS 2.1.0. Pearson correlation was used to investigate whether there is any relationship between soil temperature sensitivity with other soil properties. Then, principal component analysis (PCA) was performed to determine a minimal data set. All the statistical analyses were done with SPSS 24. Regression charts were also drawn using Excel software.

The Q10 values varied from 1.19 to 1.58. This index has the most negative correlation with organic carbon (-0.863), Labile carbon (-0.863), microbial biomass (-0.837), respiration at 25 °C (-0.831) and 35 °C (-0.8) at 1% level and negative correlation with elevation at 5% (-0.159). The principal component analysis showed that the first six components (PC1, PC2, PC3, PC4, PC5 and PC6) have special values of more than one and were able to describe 73% of the total variance. The first main component (PC1) describes 23.125% of the total variance and includes soil organic carbon, labile carbon, microbial biomass and Q10 which have the highest factor loading in this component. The second one (PC2), which explains about 12.99% of the total variance, has the highest factor loading with the vertical distance to the channel network (0.880). The third component (PC3) explains about 12.22% of the total variance. In PC3, clay has the highest factor loading. In the fourth component catchment area, convergence profile and slope length have the highest factor loading, respectively. Finally, the fifth and the sixth components are related to the elevation, slope and plan curvature.

The highest positive factor loading is related to soil organic carbon (0.981). Therefore, the first main component can be "part of the role of organic carbon in microbial biomass, labile carbon and temperature sensitivity". The results showed that Q10 has the highest negative correlation with soil microbial biomass and organic and labile carbon. In other words, the higher the soil organic and microbial biomass carbon, the lower the amount of Q10. Also, the second component can be considered as topographic indicators related to the channel network. Topographic indices can be used very strongly to model making soil organic carbon. The third component is related to clay properties. Several studies have indicated that the amount of clay has a high relationship with cation exchange capacity and it is a good indicator to determine the quality of soil. According to the results, although the correlation between some characteristics obtained from soil topographical analysis can prove the possibility of using them as auxiliary variables in predicting soil organic carbon, this point should be taken into account that other factors also play a role in the process of soil formation and development.

Gully erosion is one of the linear and progressive forms of water erosion that has an expanding role in land degradation. A gully is a channel with a steep side slope and head cut that is formed by surface or subsurface soil erosion processes though heavy rains, soil disturbance or landuse changes. Upon once the gully initiation head cut retreating is the main effective erosion process influencing land degradation and soil loess. The development of the head cuts have caused the removal of fertile soil, the aggravation of flooding by wild land and production of sediment from agricultural lands. Therefore, it is necessary to monitor the growth and expansion of gullies in order to identify the factors involved in soil loss and effectiv implimentation of soil conservation projects. In the same regard, besides paying attention to the special climatic conditions and the high sensitivity of the soil in the study area, the main aim of this research is to compare the growth of the gullies by aerial photographs and field operations to determaine the most important factors on gully head development over the short and long time spans. Also, other geometric of gullies including length, volume and surface growth were estimated and modelled by regression analysis procedure to find out the most important variables.

The present research was conducted by selecting 30 permanent gullies using a survey based on field sampling, and conducting experiments to extract data and performing statistical analysis in two areas of Ismail Abad and Najm Abad villages in Alborz province. Identification of the gullies’ location and mapping of gullies distribution were performed by using the google earth database, interpretation of aerial orthophotos of the 1980s and 1990s, digital topographic maps (25,000). Also the present location of head cuts were determined by field survay in 2017 with a GPS device. The length, width and depth of gullies, the slope of the upstream channel leading to the gully and the slope of the gully channel bed and the landuse condition were recorded during field surveying. At the end soil samples were taken from the gulleis’ walls to determine the physicochemical attributes of the soil. Using geographic information system (GIS), the longitudinal, surface and volume growth of the gullies over the three time periods of 1988-99, 1999-2017 and 1988-2017 in two study areas estimated directly. By multiplying the surface of each gully by the average depth obtained from the field surviving, the volumetric growth was also determined. Multiple regression analysis was applied into the data to determine the significant factors (soil and topography and upland contribution area and road density).

The growth estimation results showed that the average annual longitudinal growth over the 1988-1999, 1999-2017 and 1988-2017 in Ismailabad and Najmabad were 1.24, 1.18, 1.2 m/Y and 75.03, 30.78, 3.75, respectively. The average surface growths are 4.96, 4.46 and 4.65 m2/Y in Ismailabad region and 464.2, 18.73, and 187.7 m2/Y in Najmabad region. Also, the average volume growths are 4.59, 3.38, and 3.84 m3/Y in Ismailabad region and 513.86, 20.70 and 207.76 m3/Y in Najmabad region. The gully volumetric growths of Ismail Abad and Najmabad during the period of 11 years (88-99) are 45.31% and 93.82%, respectively. Also, the volume growths during the period of 99-2017 (18 years) are 54.69%, 6.18% respectively in the two regions of Ismailabad and Najmabad, and the average gully growth over the 29-year period is more than the 18-year period.

The volume growth in both regions is higher in the first phase, the average gully development in the long-term time span (1988-2017) is more than the short-term period (1988-1999), which can be due to the greater growth of the head cuts in the initial phase of formation, landuse changes, and heavy low-frequency rainfalls. Also, due to factors such as slope, type of land use, soil, and humane construction activities, the average gully growth in Najmabad region is higher than Ismailabad one. The growth (longitudinal and surface) of gullies in Najmabad region has been much higher than in Ismailabad region. In Ismailabad region, due to the conditions of hilly rugged land, the contribution upstream area of the head cuts were less than Najmabad region (alluvial plain) and ttherefore, the amount of runoff will be less and consequently the gully head development is more in Najmabad. Comparing the results of this research with other researches shows that the reason for the high growth of headcuts in this research can be related to the sensitivity of the soil, the dry climate and the human developing constarctions such as roads construction.

Estimation of the sediment load in rivers is one of the important issues in studies related to water quality and transport of pollutants, construction and operation of hydraulic structures, maintenance of reservoirs, water transmission networks, and water resources management. An accurate understanding of the sedimentation of a watershed can provide a correct understanding of soil erosion and its consequences. Since sediment changes in the river are often a function of flow discharge changes; therefore, methods of measuring suspended sediment load based on the suspended sediment concentration and flow discharge will be useful in estimating the amount of sediment load. The sediment rating curve is one of the methods that is based on flow discharge and sediment discharge and expresses the relationship between these two parameters in the form of power regression (Eq 1). (1) where Qs  is the suspended sediment discharge (in tons per day), Qw is the flow discharge (in cubic meters per second), and a and b are the coefficients of the equation . Rating curves can be drawn in different ways according to the way of data separation. Among these methods, we can refer to one-line, multi-line, mean of categories, seasonal, monthly, annual models, etc. The presence of bias in the sediment discharge relationship makes this relationship unable to show the exact sediment concentration in different flow discharges. This bias causes the amount of sediment to be underestimated. Various researchers have proposed some statistical correction factors to achieve the minimum error, which are applied in the sediment rating equation. In this research, in order to increase the accuracy of sediment estimation by using a sediment rating curve, at first, different types of rating curves were drawn for the station and, finally, correction factors consisting of QMLE, Smearing, MVUE, and (Beta) β were applied for the selected curve. Also, an attempt was made to separate the data into three categories of dry, normal and wet by using the percentage of normal precipitation and to draw the sediment rating curve for each. At the end, the results obtained from the statistical model (SRC) were compared with artificial intelligence models including two models of multilayer perceptron (MLP) and radial basis set (RBF) neural networks.

In this research, the flow and sediment discharge data from 1350 to 1397 for the Jelogir station in Khuzestan province located on the main Karkhe River were prepared from the Khuzestan Regional Water Organization. Sediment rating curve models, including common linear curve (USBR), mean of categories, monthly, seasonal, bilinear, trilinear, dry, normal and wet models were drawn for the station. Then, for the drawn curves, evaluation criteria including RMSE, ME and P were checked and, finally, by ranking these criteria, the curve with the least error was selected. In determining the rank of each model, the values of the evaluation indices were compared with each other. In this way, the closest P and ME index value to 1 and the closest RMSE index value to zero, which indicates the least difference between the estimated and observed sediment values, was assigned the first rank. In order to investigate the effect of skew correction coefficients on the accuracy of sediment rating curves, coefficients including MVUE, FAO, QMLE and Smearing were applied on the rating curve which was selected as the optimal model in the previous step. The data were processed using neural network models. For this purpose, different structures of neural networks with different layers, neurons and functions were investigated through trial and error.

According to the obtained results, the mean categories method has the highest correlation coefficient (0.85). The RMSE in rainy and flooding months (April and March) and also in high flow discharge rates (in bilinear, and trilinear models, at flow discharge greater than 201 and 114 cubic meters per second, respectively), has allocated the largest amount. The lowest value of RMSE is related to the months of August and September, which is reasonable due to the lack of rainfall and flooding in these months and as a result of low erosion of sediments. According to the ranking values, the periods of low rainfall, including summer and July, August and September are in the first ranks, and as a result, the sediment rating curve has more accuracy in estimating sediments. Finally, the rating curve of August, which has the lowest total ranking value, was chosen as the optimal curve. According to the ranking of the correction coefficients, it can be seen that the sediment rating curve without applying the correction coefficients (the highest rank) has the highest amount of error and by applying the coefficients, the error of sediment flow estimation can be reduced. Finally, MVUE with the lowest total ranking was chosen as the optimal correction coefficient, and by applying it, the accuracy of the model in estimating the sediment discharge increases. In the neural network model, Lunberg-Marquardt optimization algorithm was used and the number of hidden layer neurons in the best MLP and RBF structure was obtained as 5 and 6, respectively. Also, the activator function in the hidden layer in MLP was selected as sigmoid tangent and Gaussian function in RBF. The results show that by using neural networks of multilayer perceptron, it is possible to predict the amount of suspended sediment with higher accuracy, and the accuracy of the results obtained from the artificial neural network method is far higher than the accuracy of the rating curve method with and without data classification. According to the results, the MLP model has shown a lower error value than the RBF radial base model.

In this article, in order to estimate the suspended sediment in the Jelogir station, the data were separated into different forms and the sediment rating curves were drawn into linear curve (USBR), mean of categories, monthly, seasonal, dry, normal, wet, bilinear, and trilinear types. The obtained results showed that the accuracy of the relationship obtained for the classification of data based on August (R2= 0.785) and the total rating of 9 (the lowest value) was more than the other models. And at high flow discharge, the accuracy of the models decreases. It was found that the correction coefficients are effective in increasing the accuracy of the models, and the lowest amount of error for the optimal model is obtained by using MVUE. Comparing the results of statistical methods and neural networks showed that neural network models are more accurate in estimating daily sediment. The better performance of artificial neural networks compared to statistical methods can be expressed in the nonlinear approximation capability of neural networks.

Selection and accuracy of appropriate zoning methods and preparation of maps corresponding to variation of groundwater qualitative characteristics depend upon the region's condition and the available statistics and data. The goal of this research is to determine the most appropriate method of interpolation and spatial analysis of qualitative components such as Cl, EC, SO4, SAR, TDS and TH of groundwater in Mashhad Plain. In this research, at first, the data of 177 wells were selected with respect to their dispersion and accuracy for two consecutive years (1392-1393); then controlling and reconstruction of data were performed. Kolmogorov-Smirnov test showed that the data were not normal and to normalize them their logarithm was obtained. Then using GS+software, the best variogram model was fitted to the spatial structure of data. The results show that for qualitative parameters, the exponential, Semi-variogram and linear models are the best semi variogram models. The precision of groundwater qualitative parameters in the three methods of Kriging, Cokriging and Inverse Distance Weighting (IDW) was assessed and the precision rate results showed that Cokriging method has a higher precision and lower error. The spatial structure of studied characteristics follows the exponential, spHerical and linear model with an influence range of 9656 to 71100001m and the threshold range of 0.965 to 5.65, and the spatial dependency class was in the range of 0-0.87. Finally, the zoning maps of water qualitative parameters were prepared using GIS software. Due to population concentration in the southern parts and overuse of wells and recent droughts, the highest values of qualitative components belong to the south of the study area. According to the geological formations and the type of rocks in the northwest and southeast, the concentration of calcium, potassium, sodium, chlorine, as well as the percentage of evaporation and transpiration and temperature are more evident.

In this research, three methods of interpolation, namely, Weighted distance image, Kriging and Co-kriging are used to predict some quality indicators such as Na, TH, EC, SAR, CL, CA, Mg, and SO4 in 2013-2014. Data have been collected from 177 piezometric wells in the Mashhad Plain. In this study, after data normalization, parameters were interpolated using three geostatistical methods (ordinary kriging, co-kriging, and IDW). In order to evaluate the methods of interpolation, the mutual evaluation test was used. Then, based on the best method of mediation and the use of geographic information system, a map of some parameters and plain zoning maps were prepared.

The purpose of this research was to determine the most appropriate interpolation method in order to investigate and spatially analyze the changes in some quality characteristics of underground water in Mashhad plain. After the appropriate model was fitted, according to CO/(CO+C) observations, the strength of the spatial structure in the quality components of the studied water is not very strong. For the zoning of the quality components, the Co-kriging method was introduced as a suitable method, and considering that correlation between different variables is used in this method. It can be said that there is a significant relationship between the components of groundwater (Zahtabian et al., 2019). The results of zoning the quality of the parameters showed two inappropriate ranges in the northwest and east of the region, which about 70% of the exploitation wells are located in these areas. Also, due to the recent droughts, the process of desertification will be accelerated in this region; therefore, more attention should be paid to the proper management of underground water in this region.

 According to the obtained results, the best fitted model for CA and MG parameters is linear model; EC parameters are exponential model and NA parameters are Semi-variogram model. Regarding the spatial structure, parameters MG and TH have strong location continuity (less than 0.25) and parameters MG, NA, EC have weak continuity (more than 0.75). According to the obtained results, was used to estimate TH from anion, CL from TDS, anion from cation, CL from anion and vice versa and estimate CL from SAR.The results show that the concentration of calcium, potassium, sodium, chlorine, SAR, TH, PH is higher in the northwest and southeast, and as a result, the quality of water for various uses is lower, which is one of the possible reasons, in addition to the different formations of the earth. The geology and type of rocks (that is, the presence of gypsum materials and stones in the northwest and southeast parts or smaller amounts of these materials in the north, central and south parts (Owaisi, 2000)) can lead to higher temperature and drier environment which is the result of the higher rate of evaporation and transpiration as well as the type and density of land use (Laleh-Zari, 2019)