فصلنامه پژوهش های فرسایش محیطی
سال دوازدهم شماره 1 (پیاپی 45، بهار 1401)

Physical properties are one of the most important soil properties that affect other aspects of soil such as erosion and water infiltration into the soil. One way to improve soil properties is to use modifiers. Biochar is one of the modifiers based on organic matter that can play an important role in soil properties and the results of research studies have shown the effect of biochar on the physical and chemical properties of soil. Therefore, the purpose of this study was to investigate the effect of Shrimp and eggplant waste biochar suspension on some soil structure stability indices.

 In this study, the effect of Shrimp and eggplant waste biochar suspension on soil structure stability indices was investigated and for this purpose sandy soil was selected. The studied soil was sampled from agricultural lands around Bandar Abbas intact areas with polyethylene columns with a height of 25 and a diameter of 20 cm. The experiment was performed in a completely randomized design with 9 treatments and 3 replications. The studied treatments included biochar of eggplant and shrimp wastes suspension separately with concentrations of 0, 4, and 8 g/l which were added to the soil inside the columns according to the weight percentage. They were incubated for 100 days at moisture ranging from field capacity (FC) to 50 % FC in greenhouse conditions. Then some soil physical properties including mean weight diameter of wet sieving (MWDwet), dry sieving (MWDdry) aggregates, percentage of aggregate destruction (PAD), and saturated hydraulic conductivity (Ks) were measured. The analysis of variance for a completely randomized design was performed in order to evaluate the significance of shrimp and eggplant biochar rates on soil physical parameters by using the SPSS16 statistical software. The estimated Means were compared using Duncan’s test with a P<0.05 level of significance.

The results showed that the addition of shrimp and eggplant waste biochar suspension had a significant (p<0.01) effect on the measured parameters. In addition, the addition of both shrimp and eggplant waste biochar suspension to the soil increased MWDwet and MWDdry and decreased PAD, BD, and Ks, significantly. The maximum values of MWDwet and MWDdry were obtained by application of 8 and 4 g/l eggplant waste biochar suspension, respectively, which showed a significant difference (p<0.05) with the control treatment. The minimum percentage of aggregate destruction (PAD) was obtained by application of 8 g/l eggplant biochar suspension (21.7%), and the minimum of Ks was obtained by application of 4 g/l Shrimp waste biochar suspension (3.7 cm/h).

 The results showed that the addition of different levels of shrimp and eggplant biochar due to the presence of organic nuclei in it can act like cement, causing the mineral particles to stick to the soil and leading to the formation of stable aggregates. Organic biochar carbon in the soil with chemical bonds and the formation of bridges between soil particles as a bonding agent increased the stable bond between soil particles, increased aggregation and the formation of stable aggregates and also increased MWDwet and MWDdry. Therefore, by improving the soil properties by biochar, the percentage of aggregate destruction (PAD) can be reduced. Furthermore, the application of different levels of Shrimp and eggplant waste biochar suspension has reduced bulk density (BD) of sandy soil. It seems that the increase in bio-waste of shrimp and eggplant waste biochar, as an organic compound, has affected the amount and distribution of micropores and soil porosity with a sandy texture, therefore, reducing the bulk density of the soil. The application of different concentration levels of shrimp and eggplant waste biochar has reduced the hydraulic conductivity in the studied sandy soil. It seems that the addition of both types of eggplant and shrimp biochar by increasing more stable aggregates in dry and wet conditions and bulk density changes the pore distribution, reduces the number of macropores and increases the micropores in the sandy textures. It can also significantly reduce the saturated hydraulic conductivity compared to the control treatment that lacked any modifiers. According to the results of this research, the application of shrimp and eggplant waste biochar improved the physical properties of soil and reduced the percentage of soil degradation, hydraulic conductivity, and bulk density.

Knowing the extent of erosion in each area and the amount of sediment load they produce, especially near dams, is very important and this leads to identifying the factors affecting each area and creating solutions to control or reduce sediment load to dam reservoirs. This effort has become a necessity. Therefore, by simulating runoff and sediment in Karun basin by SWAT model and performing the calibration process and reviewing the results of the stations and their sediment load, areas with higher susceptibility to erosion were identified. After identifying the critical erosion areas and in order to reduce erosion and sediment load of the whole basin, a plant filter was used to apply the management and protection strategies of the SWAT model. Evaluation of the results in this section showed that the implementation of plant filtration in some sub-basins can reduce sediment load by up to 28%.

To build and prepare the SWAT model, inputs such as topographic information, meteorology, soil, vegetation, reservoirs and management data were used. Daily temperature, precipitation information, location of meteorological and rainfall stations with dbf or txt extensions, topographic information in the form of digital elevation model with UTM coordinate system, Grid format and location of hydrometric stations and springs in tables with dbf extension were introduced to the model as well. Maps of waterways and rivers in vector form and land use and soil maps as raster from in Arc GIS 10.4 software are included in the model.One of the parameters required to simulate the basin by the model is meteorological information. In this study, rainfall statistics of 57 rain gauge stations and 8 hydrometric observation stations with a statistical period of more than 26 years have been used in the catchment area of ​​Karun River. In this research, the characteristics of the basin have been investigated using the digital model of DEM height with a cell size accuracy of 90 × 90 m and using the GIS geographic information system. For this purpose, with the help of Arc GIS10.4 software, the physiographic characteristics of the basin such as the area and average slope of the basin and river were extracted. Based on hydrological characteristics, Karun Basin erosion studies have been divided into 49 sub-basins and 424 hydrological reaction units.In this study, the region is classified into 5 categories. Accordingly, about 25% of the area has flat land and a slope of less than 5%; 12% have a slope between 5 to 10%; 9% have a slope between 10 to 15% and 8% have a slope between 15 to 20, and most of the area has sections with a slope of more than 20%, which covers more than 45% of the area.

Implementing a plant filter that is densely vegetated to reduce runoff flow and trap sediments can be in the form of combinations of meadows, grass, trees and shrubs. In order to innovate in the application of filters, the width of filter strips in all sub-basins is not defined uniformly, but according to the area of each sub-basin, the amount of filter is considered different and the filters are applied equal to 40% of the area of each sub-basin. After applying the plant filter, the results obtained from the sediment of each sub-basin were compared against the amount of sediment in each basin before the application of the plant filter. The results showed that the implementation of the filter can reduce the amount of sediment up to 28% in some sub-basins in some areas. By comparing the figures that show the effect of plant filter application in reducing sediment in each sub-basin and the slope map of the area, it can be seen that the areas that had more slope had a relatively significant performance due to plant filter application. In addition, the application of plant filters has often reduced the sediment load and the average sediment load of the entire basin by an average of 10.4%.

The purpose of this study was to estimate the amount of runoff and sediment load produced in Karun watershed and to investigate the implementation of plant filtration as one of the most effective management and conservation strategies to reduce the amount of sediment load in the entire basin. According to the topography of the region and the slope characteristics of the region, most of the area has areas with a slope of more than 20%, which covers more than 45% of the region, and land use of the region, which uses more than 46% of forests and vineyards. It has been covered with pastures with an area of ​​about 31% of the total use basin of the region and also the existence of livestock equivalent to the area which is equal to 11.29. Erosion control in this area requires strategies to reduce this sediment load, including the construction of dams. After simulation of runoff and sediment in hydrometric and sediment measurement stations and evaluation of SWAT model by good fitting coefficients (NS, R2) and uncertainty coefficients using SWAT-CUP model, the facilities and areas with higher erodibility were identified. Then, by examining the sediment load trapped behind the dams, the sediment measurement curve was modified and adjustment coefficients were applied to correct the sediment results of the area. Then, by presenting the use of plant filter as a management solution, the sediment load of the area and special erosion were reduced. The results show that the SWAT model has a high ability to simulate all the details of the area. This feature allows the model to be simulated accurately to identify any level of details in the area that the user needs. This model makes good use of the effects of rainfall, snowmelt, irrigation and withdrawal from dams or groundwater, which led to the results of good runoff fitting coefficients of acceptable values. Also, different thresholds of information layer overlaps were estimated to determine the number of hydrological units. Baes on this estimation, 696 HRU achieved better results and the use of 7-station data in determining the parameters selected for calibration increased the possibility of better spatial variation of parameters in the final calibration of results. The results of this study are given in the daily time frame; in fact, the results are estimated in 3 days, and the results are given in the daily basis.According to the proposed maps for spatial distribution of erosion, the upstream sub-basins have less sediment and the accumulation of sediment near the reservoirs of dams is higher, which made the areas more effective. Also, as other ways, the effects of using slopes or cultivation on level lines can be examined in these areas. In addition to the slope of the region, due to the high density of livestock in Chaharmahal and Bakhtiari province, another factor of high erodibility in this area can be considered as overgrazing.Also, the results of the MPSIAC methods were closer to the results of the SWAT model than the current method of estimating the exchange rate. However, the reports of the Water and Power Company expressed the results of the EPM model closer to the results of the flood conditions.In this area, the SWAT model was not accurate enough in estimating sediment load due to the neglect of sediment volume in flood conditions in the sediment measurement curve. In order to solve this problem, in this study, the correction of sediment measurement curve by fitting two equations for flood and non-flood conditions was used.In the field of management solutions, among the solutions, the implementation of plant filters has had the greatest impact on reducing sediment load and in some sub-basins alone has been able to reduce the sediment load up to 28% of the sub-basin. Of course, in this study, a fixed value for the width of the filter strips is not considered, but for better filter performance, we considered the width of the filter strip in proportion to the area of ​​each hydrological unit, which is defined as a percentage of its area. Also, with the implementation of the plant filter, the amount of change in each HRU was investigated. The results in the percentage change scale of sediment rate showed well that the application of the filter in most hydrological units had very positive effects and had reduced sediment load with an average of 10.4%.

The word erosion means the abrasion of the earth's surface, during this process, soil particles are separated from their main bed and transferred to another place with the help of one or more transfer agents. Soil particles can be transported by water, wind or glaciers. Particles transferred and accumulated elsewhere are called sediments. Soil erosion reduces arable land fertility and causes harmful damage. Soil water erosion is a natural phenomenon that has become one of the most important environmental threats in the world today due to unwise human activities, such as the irrational transformation of the earth and the destruction of vegetation The study basin is one of the Ghezel Ozan River branches in Tarom region, which is more prone to erosion due to the special lithology of this region (marl and tuff lithology) and heavy rainfall. The purpose of this study is to investigate erosion in Jazla chay catchment, which is one of the Ghezel Ozan basins. Due to the current situation and successive droughts, erosion in this basin can cause irreparable damage to the villages of this basin. Basin erosion is estimated by comparing three models (BLM, Fargas and FSM)

Jazla Chay catchment area with an area of ​​149.61 square kilometers is under the Ghezel Ozan tributaries in Tarom region. To study the erosion in the study basin, software (Arc GIS 10.5), Google Earth and geological map of 1.100000 and topography of 1.50000 area have been used. First, basic maps including geological maps, topography, waterway, land use have been prepared. Using Arc GIS software, Fargas, BLM and FSM models have been implemented for erosion zoning in Jazla chai catchment area. A field visit was made to the study basin to observe erosion in the environment. Geological maps, land use, topography in digital Gis and land environment were referenced and using tables related to three models FSM, Fargas and BLM, the layers were scored and erosion zoning maps were prepared and then compared. The Fargas model was introduced by Fargas et al. In 1997. The following steps are required to implement this model: 1- Determining the erodibility index of the basin: At this stage, the amount of erodibility is determined for each stone unit. 2- Placing water maps and rock units and evaluating the amount of drainage density in each rock unit provided by Fargas et al. 3- Determining the risk of erosion using the evaluated coefficient for rock resistance to erosion and drainage density in each rock unit. The BLM method was developed by the US Office of Land Management. Using this model, the erosion status can be scored according to the sum of the scores of seven factors (soil mass movement, litter cover, surface rock cover, reinforced rock fragments, surface grooves, waterway shape and development of moat erosion) and accordingly the factors of the general erosion status are determined for each type of erosion.In FSM model, 5 factors of geology, topography, vegetation, moat erosion and basin shape are used to calculate the sedimentation of the basin. The scores of each factor are shown as low, medium and high with the numbers 1, 2 and 3, respectively, which are determined using field navigation, topographic and geological maps. After scoring the five factors, the value of FSM coefficient will be obtained by multiplying the factors in each other and using this coefficient, the erosion rate of the basin will be calculated

To evaluate the BLM model, seven factors have been considered, of which 4 factors are related to surface erosion, one factor is related to furrow erosion, one factor is for canal erosion and the seventh factor is related to moat erosion. Mountains and foothills have high erosion and are moderate in the plain. The slope factor changes significantly from one piece to another. The average slope in these different parts is not equal and its amount gradually decreases from the upstream to the downstream parts. Adjusting the balance profile from the bottom up means that most of the reversal operation (regressive or ascending erosion) takes place. In the study basin, the slope increases from downstream to upstream, which indicates the dominance of regressive erosion in the basin. In the BLM model, it can also be seen that erosion has increased in the upstream part (mountain and foothills unit). The amount of slope varies in different parts based on the available lithologies, which according to the lithologies of the basin, the amount of slope increases from the downstream, which is the lithology of the conglomerate, to the upstream lithology, which is sandston tuff, tuff and silt . The profile of the studied basin is drawn from upstream to downstream, which indicates an increase in slope upstream.  Fargas model is based on geology and two factors of erosion (drainage density factor) and erodibility factor (rock susceptibility factor to erosion) which according to Fargas model, erosion in the catchment area Jazla chay is violent. The FSM model uses 5 factors of topography, lithology, ditches, basin shape and vegetation that have different scores and is estimated through topographic and geological maps and field visits and according to the model. Erosion rate in Jazla chay catchment is estimated to be 699.08 tons per square kilometer per year.

Given the current situation in Iran, which is threatened by drought and severe erosion, the assessment of erosion in its sub-basins can determine the rate of erosion in different sectors. In this study, Jazlachai basin has been selected from Ghezel Ozan sub-basins in Tarom region (Zanjan) to investigate the severity of erosion. To measure the intensity of erosion in the study basin, three models Fargas, BLM and FSM have been used. The results obtained from the Fargas model, based on the geology of the basin, show that the maximum area of ​​the basin is 82% with severe erosion and 13% with erosion. High and 5% has very severe erosion. Results of BLM model (using seven factors, 4 of which are related to surface erosion, one factor related to furrow erosion, one factor for canal erosion and the seventh factor related to moat erosion) in Jazlachai catchment in three units Topography (mountain, foothills and plains) indicates that the intensity of erosion is moderate in the plain unit and high in the mountain and foothill units. Evaluation of basin erodibility using FSM model in 5 factors of vegetation, basin shape, topography, lithology and ditches in the basin indicates that the basin erodibility rate is equal to 699.08 tons per square kilometer in Is the year. Among the factors that are effective in the intensity of erosion and sediment production and flooding of basins, the physiographic and topographic characteristics of the basin, including the condition of waterways, height and slope of the basin. The central part of Jazlachai catchment has a high slope, the slope factor increases the erosion in this part by increasing the speed of water flow. The western and southwestern part of the basin has a gentle to steep slope and the formation of this part is semi-resistant. As can be seen in the map obtained from Fargas and BLM models, this area has high erosion and regressive erosion dominates in the study basin and leads to further erosion of the main riverbed. According to the field visits and the results of the implementation of these models, the BLM model is more compatible with the erosion that prevails in the Jazlachai catchment.

Gully erosion is an advanced form of water erosion that needs more research, given the vast mass of soil degradation and its subsequent impacts. An accurate evaluation of gully erosion susceptibility based on the density area model can help identify and predict the areas with advanced gully erosion in the units and classes of each factor. Also, the TOPSIS model and proper prioritization of erosion susceptibility at sub-watershed can be critical for conservation activities. Researchers thus far have used the TOPSIS model to estimate soil erosion in a watershed or evaluate the factors affecting soil erosion intensity, disregarding its potential to prepare efficient gully erosion susceptibility maps. However, the study area, located in the northeast of Golestan, a province in northern Iran, is susceptible to gully erosion due to the horrific deforestation for agricultural and animal husbandry purposes and the expansion of loess soil. Watershed operations are imperative to prevent the spread of this type of erosion, although since the credits are limited, further preventative measures should be prioritized.

This study aimed to prepare a gully erosion susceptibility map of the region using the density area model as well as prioritizing sub-watershed based on the TOPSIS model. It has also been attempted to evaluate their efficiency to prevent time and capital resources waste by identifying erosion-susceptive watersheds and implementing conservation programs as required. The Chenarli watershed is located northeast of Kalaleh in Golestan, Iran. The average elevation of the area is about 628 meters. The elevation, slope, aspect, lithology, vegetation, and land use have been used in this study to prepare gully erosion susceptibility maps. The layer of the area's lithology units was obtained from the 1:100,000 map of the geological survey of Iran, and the raster layers of elevation, slope, aspect of the digital elevation model (DEM) were obtained from an area of 30 m2 of the region. Landsat images were used to prepare a vegetation map and land use area. The Gully erosion distribution map of the region was created using Google Earth images, including 93 gullies, out of which 64 gullies were used for susceptibility map preparation and 29 gullies were used for map validation. The density area model was used to determine the weight of the effective factors. In this study, the layers of independent variables overlayed with gully erosion distribution in ArcGIS. The gully density was calculated in each class of factors. Technically the TOPSIS model, one of the most famous multi-attribute decision models, prioritizes gully erosion susceptibility at the sub-watershed level. In this research, sub-watersheds have been regarded as the criteria affecting gully erosion. The T output of the measured density area model was used as a reference map to evaluate the validity of the model results. Also, the coefficient of determination (R2) was calculated.

In this study, the frequency of gully erosion in the region was identified by overlaying the factor maps, including elevation, slope, aspect, lithology, vegetation, and land use, by gully distribution map in ArcGIS, and the weight values of the classes of each factor were calculated using the density area model. The relationship between topography factors and gully erosion in the region indicated the higher sensitivity of lower elevation classes, lower slopes, and western and southern slopes. Calculation of gully surface density in different rocks showed that shale and loess were more susceptible to gully erosion, respectively. Furthermore, and in terms of land use, the highest amount of gully erosion was observed in farmlands and rangelands, but in areas with natural forests, gully erosion susceptibility was negligible. After calculating the weight of each class of factors affecting the occurrence of the gully in the region, the weight maps were overlayed, and the raster map of gully erosion susceptibility was prepared using the study model. The results of the final map of the density area model indicate that 11.61% of the area has a significantly low susceptibility, and 23.88% are in the low class, 23.03% in the middle class, 25.4% in the high class, and 16.1% of the area is in the very high susceptibility class. The TOPSIS model was used to prioritize gully erosion susceptibility at the sub-watersheds of the region. For this purpose, the study watershed was divided into ten sub-watersheds. Then, the score of the effective variables, the same variables in the density area model, was determined separately. The calculation of the proximity coefficient based on the TOPSIS model showed that sub-watershed no. 2, with a coefficient of 0.791, had the highest gully erosion susceptibility, and sub-watershed no. 6, with a coefficient of 0.144, had the lowest gully erosion susceptibility.

Gully erosion susceptibility map of Chenarli watershed was prepared by overlaying weighted maps of effective factors using the density area model.  The empirical probability was calculated for the model (KS=0.913), indicating the high accuracy of this model in preparing the gully erosion susceptibility map of the region. The density area validation model was used as a field reality map, and the coefficient of signification (R2) was calculated to compare and assess the TOPSIS model's accuracy. The coefficient of determination in this study shows the level of coordination and relationship between the results of the TOPSIS model and real data (the output of the gully erosion density area model). The results displayed a significant and appropriate relationship between the results of the TOPSIS model and density area model in the region since this model with the coefficient of determination equal to (R2= 0.597) could predict the susceptibility to gully erosion at the sub-basin level, indicating its ability to prioritize the susceptibility of gully erosion of sub- watersheds. It can be concluded that recognizing gully erosion susceptibility in classes of the effective factors using the density area model and introducing erosion priorities of sub-watersheds with the TOPSIS model can help improve the selection of gully control and soil conservation methods and contribute to the required operational focus on the field.

More than two thirds of Iran's area is covered by arid and semi-arid lands. Lack of rainfall in these areas has reduced its ecological diversity and low-density vegetation has been established on it. Lack of vegetation allows the wind to easily erode the soil surface and annually carry large amounts of topsoil from one point to another (Ahmadi, 1999). Finding the source of sediment is one of the basic principles of controlling and combating soil erosion, because by identifying these areas, instead of addressing the problems, the causes can be identified and control erosion activities can be concentrated in source areas (Feng et al., 2011). Knowing the origin of sand dunes is one of the most important examples of soil management in order to optimize exploitation, reduce degradation and conduct wind erosion control plans. Due to the problems of traditional methods, the fingerprinting method as an alternative and appropriate method has been considered by various researchers. Therefore, the main purpose of this study, considering the importance of land uses in erosion and sedimentation process, is investigating the role and importance of dominant land uses in Abu Ghovir region, in Dehloran city of Ilam province, in producing sediment of sand dunes by using fingerprinting method.

The study area is located in Abu Ghovir plain with an approximate area of 19650 hectares in the southeast of Ilam province and on the banks of Doviraj River, at 47° 31' 29'' to 47° 55' 01'' east longitude and 32° 10' 06'' to 32° 24' 19'' north latitude. First, by studying the wind rose of the Dehloran synoptic station during the 25-year statistical period (1992-2017) for the prevailing wind in the study area and using the images received from Google Earth, the study area was determined.Then, by using Landsat 8 satellite imagery and land use map, the existing land uses in the study area including sandy dunes, agricultural lands, Doviraj River and rangelands were determined. After that, 5 soil samples from each land use including rangeland, agriculture and riverside lands as sources of sediment and from sand dunes as sedimentation area from a depth of 0-5 cm were collected.After drying in the open air, the soil samples were passed through a 2 mm sieve and physical and chemical tests of the soil were performed by the following methods. Soluble cations including Ca and Mg by complexometric titration, Na and K soluble by atomic flame emission method (flame photometry) (Rhoades, 1982), soluble chlorine by sedimentation titration using silver nitrate and also carbonate and bicarbonate by simple titration of acid and base were calculated. The elements Fe and Cu were also read by the atomic absorption apparatus (Lindsay, 1979).In order to select the initial detectors, the normality of the data is first checked by the Smirnov Kolmogorov test. If the source characteristics are normal, the quality of the source characteristics of sediment source in the study area is applied by using a two-stage statistical method (Collins et al., 2001) to select the optimal combination of traces in sediment source.

Examination of Kolmogorov-Smirnov test values in the studied land uses shows that the distribution of the studied variables is normal, so parametric statistics can be used to compare these elements in different land uses. To evaluate the ability of detectors in separating work units including rangeland, river and agriculture, Kruskal-Wallis statistical test and stepwise detection function test were performed. The results of Kruskal-Wallis statistical test was not suitable, but the stepwise detection function test was introduced to measure the optimal detector for separating the three sources from the 9 detectors (Na, Cu, Fe, Mg, Ca, Cl, Sulfate, Carbonate, Bi carbonate) in order to select the source of sand dunes including rivers, pastures and agricultural lands. Based on the results, Na and Cu elements were selected as optimal tracers and, in general, 80% of the source samples were correctly classified by these two optimal tracers. To evaluate the different patterns of spatial displacement of the three sediment sources, the distribution diagrams of the first and second functions calculated by the regression method of detection analysis based on the optimal combination of two geochemical tracers were used. The results showed that all three sediment sources were well separated based on these detectors.

The results of granulation and studies conducted showed that there was a very close genetic relationship between sediments in the source area and wind deposits, which indicated the localization of the particles origin and their displacement in the field. Statistical tests showed that Cu and Na detectors have the ability to distinguish different land uses; therefore, these elements are the best source characteristics for the region. The elements in this composition are geochemical elements, so only these elements can be used for source origin studies and determining the share of land uses, and there is no need for other elements used in this research. Finally, by using this combination and hybrid models, the contribution of each land use in the sediment producing process was determined. The results of hybrid models showed that the share of river, rangeland and agricultural land uses was 99.24, 0.76 and 0%, respectively. The relative error of the hybrid model was calculated to estimate the share of different land uses for sediment samples, which was equal to 1%, and the efficiency coefficient of the model was 99%. Due to the fact that the calculated relative error was low and the efficiency coefficients were close to 1, the accuracy and efficiency of the model was confirmed. According to the results, river, rangeland and agricultural land uses had the highest and lowest share in the sedimentation of sand dunes in Abu Ghovir region, respectively. In general, the method of wind sediments origin was able to determine and properly separate the share of land uses in the study area.

Dry and ultra-dry conditions, prevailing in a large part of Iran with less than 710 mm of rainfall per year, have caused about 40 million hectares of the country to cover desert areas, sand dunes and areas with low vegetation (Refahi, 2004). Nebkas are very important in stabilizing mobile sands in arid and semi-arid areas and protecting human settlements and facilities to some extent from the onslaught of wind sands. They are found and usually formed in semi-arid, hot and dry and hot and humid areas (Amini et al., 2011, quoted by Thomas and Tousar). Nebka plays a very important role in the stability of ecosystems in arid and semi-arid areas. One of the species that has high resistance to wind and its roots and stems settle large volumes of aerosols and wind sediments and fights against wind erosion is Choug species that can stabilize quick sands in the south of the country. No studies have been conducted on this species. Therefore, this study aimed to investigate sediment changes in relation to vegetation in sediments and its comparative analysis against wind erosion to evaluate the process of stabilization of quick sands.

 In this research, one-dimensional sampling method and longitudinal unit have been used. This method allows random sampling of nebkas throughout the study area. After identifying the study areas and determining the scope of development of nebkas, by field reference, sampling and measurement of morphometric components of nebkas were performed. Sampling was done with 5 representative areas, 10 transects of 1000 meters with a distance of 500 meters from each other and perpendicular to each other. To determine the starting point of transect and the beginning of sampling, transects were selected so that they transversely cover the study areas. In each linear transect, 5 plots with dimensions of 4 * 4 meters were placed equal to 50 plots at each transect. The size of plot was determined by minimal area method. A total of 10 transects and 50 plots were used in each area; vegetation measurement was performed on totally 250 plots put at the region. Then, to start the sampling, the points were selected by GPS as an indicator at equal distances from the start of the Nebka landscape in the 5 areas under the study. Two Landsat satellite images from 1990 and 2020 were also used to determine a mount of morphometric changes in the sampled nebulae over a 30-year period. Then, using Envi 4.7 software, the region's nebkas and other existing uses were determined. Finally, after preparing land use maps, the area in the region was surveyed. 3-1: Measurement of morphometric properties of nebkas At this stage, the morphometric characteristics of Nebka such as sediment volume, dune height, dune base diameter, nebka slope, plant height and canopy diameter were measured. The basis for measuring the morphometric components of Nebka is as follows:In order to measure the height of Nebka, the height of Nebka peak to the level of its base and for the diameter of the base of Nebka, the average diameter of the base were measured by a tape measure. The slope of the Nebka cone is calculated by means of a slope gauge and the volume of the Nebka cone is calculated by Equation (1). Also, in order to obtain the average height of nebkas in the main and larger habitat, after many rounds of forest, the total height of nebkas was measured in all sample plots of one hectare that were systematically randomly distributed in the habitat (Zobeiri, 2009). Then, the ratio of canopy area to nebka area (in percentage) and the volume of each nebka were estimated using Equation (1).

 The relationship between plant traits and Nebka morphometric traits was estimated based on correlation analysis and multivariate regression analysis. In order to compare and evaluate the measured parameters of sediment and plant, to investigate the relationships between them and to perform the mean comparison test to evaluate the differences between the measured parameters in the study site, EXCEL 2013 and SPSS16.0 software were used. In order to compare the indicators studied in the study, Pearson correlation test with 95% confidence level was used to investigate the trend of their changes. In order to investigate the correlation coefficient and the explanatory relationship between plant morphological indices and sediment morphometric indices, after determining the non-normality of the data, the data were normalized by multiplying each data by 2. After normalizing the data, considering the indices measured from Nebka as a dependent variable and the indices measured from the plant as an independent variable, the regression relationship was examined and also the degree of correlation between the different measured indices was computed. 4- Findings (Results) 4-1 Results of correlation study between variables The results of correlation between sediment and plant variables in the Sirik region showed that there is a significant correlation between all measured variables except the canopy diameter of the plant at 95 and 99% levels. 4-2 Multivariate linear regression

The results of multivariate linear regression using independent variables of plant height and canopy diameter in the Sirik region showed a significant relationship for estimating dune height and sediment volume. Based on the results, plant height was the most important and effective independent variable in estimating the height and volume of dune sediment in the old Sirik region. 4-3: The rate morphometric changes of the measured nebulae during a period of 30 years The results of the study and classification of Nebka in the Sirik region during the 30-year period showed that the rangeland use has undergone the most changes and the least amount of changes were related to areas with Nebka. In addition, the study area has been affected by wind erosion over time, which has reduced the area of ​​nebkas in the study area by 8 hectares.

The results of multivariate linear regression using independent variables of plant height and plant canopy diameter in the study areas showed that there is a significant relationship between sediment height and nebka volume only in the Sirik region. Based on these results, plant height was the most important and effective independent variable in estimating the height of sand dune and volume of Nebka in the old Sirik region. These results were in line with the findings of Glaze et al. (2014), who studied sediment transport recovery in Nebka vegetation and uncovered Nebka vegetation and wind speed influences. Examination of sediment relations between flowing dunes and plant species in southern Thailand showed that sediment in windmills in the direction of wind and at the foot of phanrophite plant species was more than hemicryptophyte and criptophyte species and in the opposite direction of wind. Considering this fact, the sediments at the foot of Salvadora persica phanrophytic species in this study, along with the species, was more than Caparis decidua or Alhaji camelorum; this fidning confirmed a similarity in the results of these two studies. The results of Bobenzer (2020) in Egypt confirmed that the level of sand stabilization with a linear pattern has decreased, which is in line with the findings of this study. Valentini (2020) also stated that the changes in the use of Nebka habitat near the coast in the northern part are decreasing and in the southern part are increasing, so that the level of Nebka habitat is generally growing; these findings are not in line with the findings of this study. Also, the results of Hogan Holtz (2012) showed that the areas stabilized by quick sands, especially active nebulae, are in an increasing process and have deposited more sediment and quicksand than the last four decades; in fact, their findings contradicted the findings of this study.

Soil erosion due to rainfall is influenced by various factors such as rainfall characteristics, slope, soil properties, land use, and other related factors. The aim of this study was to evaluate the effects of different levels of slope and rainfall intensity on runoff and sediment characteristics by using a rainfall simulator in the laboratory.

In this study, changes in parameters of runoff volume, runoff coefficient, sediment concentration, and soil erosion due to changes in two slope factors at three levels of 1, 3, and 5% and rainfall intensity factor at five levels of 45, 37.5, 30, 22.5 and 15 mm hr-1 were measured using a rainfall simulator. Analysis of variance of the parameters measured in the experiments was performed with three replications in a factorial design in a completely randomized design using SPSS 23 software. Duncan's mean comparison test was also used to evaluate the significance of the difference between the means at the level of 5%.

The results of the analysis of variance indicated the significant effects of different levels of slope and rainfall intensity on the values of the measured parameters including runoff volume, runoff coefficient, sediment concentration, and soil erosion at the level of one percent. The results of the Duncan test also showed a significant difference at the level of one percent for all measurement parameters, so that the highest and lowest increase in mean values for increasing the slope from 1 to 5 percent related to soil erosion and runoff coefficient which were equal to 182 and 5 percent, respectively. Also, the greatest change among the measurement parameters occurred in the average amount of soil erosion (116% increase) from a slope of 1 to 3% with a 41.75 gr increase in soil erosion. With increasing rainfall intensity from 15 to 45 mm hr-1, the average of all measured parameters including runoff volume, runoff coefficient, sediment concentration, and soil erosion increased by 341, 20, 635, and 446%, respectively. The highest difference between the measured parameters occurred in changing the rainfall intensity from 15 to 22.5 mm hr-1 to the extent that runoff volume, runoff coefficient, sediment concentration, and soil erosion have increased 587 ml, 7%, 2 g lit-1, and 36 g with change in rainfall intensity from 15 to 22.5 mm hr-1.

The results of the present study showed a significant difference between the mean parameters of runoff volume, runoff coefficient, sediment concentration, and soil erosion at different levels of slope and rainfall intensity treatments at the level of one percent. The highest and lowest increases in mean values for increasing slope from 1 to 5% were related to soil loss and runoff coefficient and were equal to 182 and 5% increase in their mean values, respectively. With an increase in rainfall and its intensity from 15 to 45 mm hr-1, the values of all studied variables, namely, runoff volume, runoff coefficient, sediment concentration, and soil erosion increased by 341, 20, 635, and 446%, respectively. The highest soil erosion was measured at a slope of 5% with a rainfall intensity of 45 mm hr-1, equal to 160.1 gr, and the lowest at a slope of 1% with a rainfall intensity of 15 mm hr-1 and equal to 18.2 gr.

Erodibility, which is determined by the soil's intrinsic features, is one of the most important elements in soil erosion. This factor reflects how sensitive the particles of a particular soil are to separation and transmission by erosion causes, both quantitatively and qualitatively. For measuring soil loss, the Universal Soil Loss Equation (USLE) is very useful. Sources reveal that erodibility is influenced by a variety of physical and chemical features of soil. In several soil erosion and sedimentation models, such as USLE, RUSLE, and MUSLE, one of the essential parameters is erodibility, which is represented as K. Particle size distribution, organic matter, structure, and permeability all have a role. The goal of this research was to quantify the amount of erodibility (K) in dry and semi-arid soils, as well as the physicochemical parameters that influence it. Another purpose of this research is to develop a connection that uses principal component analysis (PCA) and linear multivariate regression to estimate the quantity of soil erodibility based on effective physicochemical parameters.

The research location is 20 kilometers from Yazd city, along the Yazd-Ardakan road, on the edge of the dunes facies, which includes bare, mantled, and covered pediments. Using the stratified random sampling approach, soil samples were gathered to a depth of 10 cm within the facies in this study. The size and form of aggregates, as well as water penetration in the soil, were used to calculate soil structure codes using Wischmeier and Smith's tables. In the desert, soil permeability was assessed using double cylinders based on the ultimate infiltration rate. The hydrometer technique was used to determine the spread of soil granulation. Wet sieving and the Walkley and black methods were used to assess the proportion of extremely fine sand and organic matter, respectively. Lime was calculated by multiplying the volume of the hydrochloric acid neutralization reaction by the quantity of neutralizing agents. Statistical indicators such as mean, minimum, maximum, and standard deviation were derived at this step after computing the soil erodibility index. Principal component analysis was performed using SPSS17.0 software, and the linear multivariate regression model was utilized to predict soil erodibility index. After selecting significant components, linear multivariate regression between these components and soil erodibility was conducted concurrently. The coefficient of determination was used to assess the equation's accuracy in this investigation (R2).

The findings of the physical and chemical features of soil study revealed that the texture of the soil is mostly light sandy to loamy, with low organic content and calcareous. In terms of structural form, the analyzed soils were extremely fine granular and spongy, and their structural code was based on USLE (2 and 1). The permeability of the soil profile was high to extremely high (18.4 cm/h), and it was often in Class 1, 2, and in some instances Class 3 according to USLE. In the three naked, mantled, and covered pediments, the estimated erodibility indexes based on Wischmeier and Smith regression relationships were 0.0385, 0.03, and 0.0199 ton.hr/MJ.mm, respectively. According to the particular values acquired from the parameters and the percentage of variance, the top three components may be picked as the major component using principal component analysis. The first, second, and third components have correlation values of 0.88, -0.04, and 0.41, respectively, with the soil erodibility index. As a result, the first component has a stronger relationship with the soil erodibility index than the second and third ones. The percentage of sand and silt, soil permeability, and percentage of clay have a higher correlation with the soil erodibility index, respectively, and the correlation of other factors (organic matter, gravel, fine sand, and lime) is low in this component, according to the values for the given loading period. The amount of sand in the soil and its permeability are negatively correlated; whereas, the percentage of silt and clay in the soil is positively correlated. The maximum load is connected to the variables of gravel and lime in the second component, and it is related to organic matter and extremely fine sand in the third component. The effect of characteristics on soil erodibility is significant (0.001>p) and its coefficient of determination (R2) is 0.88 percent, according to an investigation of the relationship between soil erodibility and principal component values obtained from PC1, PC2, and PC3 using a linear multivariate regression model.

 The quantity of erodibility in dry and semi-arid soils, as well as the physicochemical parameters that impact it, were investigated in this research. Using principal component analysis and linear multivariate regression, a link was found to estimate the quantity of soil erodibility based on the effective physicochemical parameters. Because of the high amount of sand in the region's soils, these soils are readily separated due to poor adhesion, but because they contain bigger particles, they resist runoff and hence create less sediment. This barrier to transfer reduces as the quantity of clay and silt in the soil increases, and consequently more sediment is transported. Furthermore, a considerable quantity of sand improves soil permeability and reduces runoff. However, when the amount of silt and clay in the soil increases as a result of surface sealing, permeability reduces and greater runoff occurs. Soil erodibility is additionally influenced by organic content, lime, gravel, and permeability. Lime has a negligible influence on soil erodibility since it contains calcium cation, which increases particle homogeneity and hence increases soil resilience to rain drops. Organic matter has a negative relationship with soil erodibility as well. The breakdown of aggregates is slowed by increasing the quantity of organic matter in the soil. As a result, as organic matter levels rise, the rate of aggregate decomposition in a particular soil falls by one-third. Similar research studies in other semi-arid and arid soils in Iran are required to provide a more reliable connection for forecasting erodibility of soils in semi-arid and arid locations.

Erosion is one of the main factors restricting the soil fertility and dust production, in several parts of the world, including Iran, has effects on climate agriculture, and human health. Controlling wind erosion would be more effective once sufficient information concerning the effective factors is available. Soil Erodible Fraction (EF) is one of the soil properties that shows the sensitivity of soil particles to wind erosion. The current research aimed to utilize ANN methods and integrating it with GA in order to estimate the soil erodible fraction to wind erosion. Allahabad plain in the southwest of Abiek city in Qazvin province is considered as one of the areas sensitive to wind erosion with strong wind direction from southwest to northeast. The drying up of Allahabad wetland will intensify wind erosion in the region and turn it into a crisis. Determining the extent of land erodibility and identifying its factors affecting can be the basis of a comprehensive plan for soil protection and land sustainability and prioritizing its implementation steps. The present study was conducted to use artificial neural network methods and combine it with genetic algorithm to estimate the soil erodible factor.

In the study area, which was part of the Allahabad plain in Qazvin province, between the coordinates of 50°15 ́- 50°57 ́ east longitude and 35°53 ́- 35°57 ́ north latitude, 95 samples were taken from 10 cm of soil surface. In the samples, the percentage of aggregates with a diameter of less than 0.84 mm as an indicator of EF and percentage of clay, sand and silt, soil saturation capacity, pH, EC, SAR, equivalent calcium carbonate (CCE) and organic matter were measured as input to the models. In this paper, to model the EF using early available characteristics, two methods of artificial neural network (ANN) and its integration with genetic algorithm (GA-ANN) were employed in order to optimize the weights. In this regard, the data were primarily divided into three categories as follows: 60% of the data series was allocated to training, 20% to validation, and 20% to network testing. In this study, MLP networks were used to model the artificial neural network in estimating the values ​​of soil erodible Fracion. In this structure, each artificial neural network includes inputs and hidden and output layers. During the learning process, the degree of network learning by the objective functions was regularly evaluated and networks with the lowest error rate were accepted. To determine the optimal network with the highest level of performance of all stimulus functions defined in the software (axon hyperbolic tangent, axon sigmoid, axon linear hyperbolic tangent, axon linear sigmoid, axon bias, linear axon and axon) by trial and error The most results were used. Levenberg-Marquardt training functions were used to teach defined networks. In this study, genetic algorithm was used to find the optimal point of complex nonlinear functions in combination with artificial neural network (GA-ANN). The genetic algorithm optimizes the weights of the artificial neural network. In fact, the objective function of the genetic algorithm is a function of the statistical results of the artificial neural network.

The results showed that the erodible fraction of soil with five soil properties including pH, electrical conductivity, SAR, clay and organic matter, had a significant correlation at the level of one percent. The models used did not have an appropriate accuracy in estimating EF in both training and testing stages, so that the highest R2 was obtained in the artificial neural network model (0.49) with test series data. Both models were slightly overestimated and the GMER values ​​for the ANN and GA-ANN models were 1.15 and 1.08, respectively, but according to the AIC index, both models had similar predictive power. Sensitivity analysis of the data showed that the greatest effect on EF in the ANN model was related to organic matter (4.07) and in the GA-ANN model was related to clay (8.14).

In the current research, the relationship between soil chemical characteristics and EF might be attributed to their previous effects on vegetation in the region. Additionally, regional evidence indicates the same finding. The highest correlation was observed between EF and soil organic matter. Based on the sensitivity analysis, in the neural network model, the greatest effect on erodible fraction was related to organic matter, pH, and EC, respectively. The effect of pH and salinity on EF could be interpreted due to their effects on vegetation and consequently, the effect of vegetation on aggregates.  An important issue in the research was that the proposed models, which were ANN and its integration with GA for estimating the soil erodible fraction, were not efficient enough for obtaining the highest coefficient of determination (R2) in the model in the neural network in the test phase (R2 = 0.49), which has an accuracy of less than 50% for estimating EF.

So much of the river sediment occurrs due to bank erosion. Understanding and predicting bank erosion is a vital requirement for river management. During  a  flood, river flow shear stress exceeds river bank materials shear strength and causes a meandering. To prevent a river accresion in a floodplain, it is necessary to understand the river and perform river management and organization operations, especially in erosion reaches. Bank erosion occurs commonly during a relatively long reaches of the river. Due to the high cost, stabilization practices are only possible locally. In order to reduce this phenomenon, it is necessary to create vegetation in river reaches. The Bank Stability and Toe Erosion (BSTEM) model is one of the most widely used models in the world in most river engineering projects developed by the National Sediment Laboratory in Oxford, Mississippi, USA. The BSTEM model was initiallly developed by Pallen Benkhead and Simon to investigate riverbank stability under vegetation and water conditions.Subsequently, in BESTEM model, various plant and hydraulic parameters such as slope patterns, roughness, angle, and pore water pressure are used. BSTEM is a bank erosion model that examines hydraulic processes, toe erosion and bank failure in soil homogeneous layers and is a suitable tool for determining riverbank conditions in order to protect river from streamflow erosion. A review of the research studies conducted in this regard showed that although the BSTEM model is a model with a long history and validity (based on numerous projects, articles and dissertations) and had been used in different countries and important rivers of the world to study the stability and bank erosion. In Iran, few studies have been done on the BSTEM model, which shows the need to conuct further studies for the purpose of validating this model. Therefore, this study was designed using the BSTEM model to investigate the stability of the river bank with respect to existing plant species in a section of the Bashar River in the city of Yasuj.

In this study, the Bank Stability and Toe Erosion (BSTEM) model has been used to investigate the stability of the reach of Bashar River in Kohgiluyeh and BoyerAhmad Province. This model is one of the most comprehensive models used for river engineering and management operations in the world, which besides stability, the failure rate, safety factor, erosion rate, sediment loading rate, and river accrestion rate are calculated. The appropriate reaches of the Bashar River for modeling, should have the same characteristics in terms of morphology, geology, land use and have a suitable length (300 meters). The study was condcuted by analyzing two parts: control (without cover, length = 300 meter) and experimental (with cover, length = 300 meter). Because of using the BSTEM model, it has been necessary to select several sections in the selected range. For this purpose, topographic maps and aerial photographs of the area were prepared. Then, with the help of these maps and Google Earth satellite images, a reach of the Bashar River was selected near the Darshahi region for modeling. Then, the selected reach was divided into two parts: control (without cover) and experimental (with cover) with the same length of 300 meters, and each part was divided into 3 sections of 100 meters. Shear strength of tree species was measured in cross-sections. The reserachers performed data comparison analysis with HEC-RAS software and BSTEM model.

The results of flood simulation in the HEC-RAS model in section 1 with cover reach and section 4 without cover reach showed that, in a 2-year flood, the average flow velocity in the middle of the cross section is 2.18 and 1.41 m/s, respectively (the estimated speed of the HEC-RAS model), and the flow height were 1.39 and 2.72 meters, respectively. The results of this study also showed that in terms of stability, there is a difference between sections with land cover (Fs <1) and no cover land (Fs <1). The results also pointed to the fact that the average width and volume of failure of the river bank in the no land cover section was 2.72 meters and 6280 cubic meters, respectively, and in the land cover section was 1.42 meters and 1686.33 cubic meters.

The results showed that in terms of stability, there is a significant difference between covered and no covered sections because of the safety factor, which confirmed the instability of the studied reach. Based on the results of uncovered sections, due to fact that their geometric condition are unstable, the need for river management operations intesifies. The results showed that for sections with vegetation, the safety factor is higher than one, which indicated the bank stability in the studied reach, while in sections of no cover, the river is unstable. Increasing tensile crack depth, and bank angle resulted in bank instability, and increasing river flow level, groundwater level and suction matrix can lead to bank stability. In order to prevent bank river erosion, cultivation and growth of plants that are compatible with the climate and the erosive conditions are necessary. Therefore, it is suggested that native plants and other various plant forms be used to prevent bank river erosion. In addition, comparative studies need to be performed on other species in order to determie the role of plant cover on river bank stability in other rivers.

Subsidence is a morphological phenomenon that occurs under the influence of land subsidence motion. The cause of this phenomenon may be due to human and natural factors. The phenomenon of subsidence in recent decades has created many problems for agricultural lands, residential areas, roads and water supply canals in some parts of the country. In recent years, the decrease in rainfall and the increase in uncontrolled groundwater harvesting by exploitation wells have caused a large drop in groundwater level, which has resulted in subsidence and cracks and fissures in parts of the Harris plain. In this research, in order to determine the affected area and finally estimate the amount of subsidence, the radar interferometer method has been used. The advantages of this method include very high accuracy, wide coverage, high spatial resolution and no need for field work, cost-effectiveness and the possibility of accessing information in any weather conditions. ENVISAT and Sentinel radar satellite images were used for this purpose. Also, two Goldstein and Adaptive filters were used to evaluate the obtained results. Also, to validate the research, the relationship between subsidence and the depth of wells in the region was studied, which showed a positive correlation of 87%; this indicates that the depth of wells is also deeper in the areas where subsidence has occurred.

Software of SARscape5. 2 and SNAP6. 0 used in this research are among the powerful tools for monitoring subsidence. The radar interference method by comparing the phases of two radar images taken from an area in two different times is able to determine changes in the earth's surface in that time period.  The phase obtained from a complication on the ground is proportional to its distance from the radar sensor. Therefore, changing this distance affects the measured phase. An image called an interferogram is created using a radar interferometry technique. To eliminate the topographic effect, the digital elevation model SRTM has been used with a spatial resolution of 90 meters. Orbital errors were modeled by a procedure that have no displacement. Atmospheric error can be adjusted with the help of atmospheric information and atmospheric model. Interferometry is produced by the complex multiplication of one SAR image in the mixed conjugate of the second image. The resulting differential interferometer contains some noise. The cause of these noises can be different, but there are two main factors influencing their occurrence. The first factor is related to the time difference between the two main and dependent images. The second main factor influencing the occurrence of noise is the spatial baseline where the amount of noise in the images is directly related to the spatial baseline. Goldstein and Adaptive Window Filters have been used to remove and reduce noise. As mentioned earlier, Goldstein and Adaptive filters were used in this study, and you will see the main results of these filters below. In principle, the differences between the results of these two widely used filters in the field of radar are discussed. The imageshave been used in the new 2017 to 2019 returns. Coherent image is an image that is resulted from the power correlation of two coordinated images. This image shows the correlation index of signal strength values ​​in two images taken at two different times. The value of correlation varies from 0 to 1, which affects the quality of the interferometry process. After interfrogram processing, to determine the average subsidence rate in the time period of the images, the time series analysis method was used with the least squares method. Based on this, the average subsidence rate is equal to 1. 2 cm. Due to subsidence in this area, it is possible to use groundwater resources indiscriminately and reduce rainfall, which has led to a drop in groundwater levels. Of course, the type of cultivation in the region and the weight pressure caused by human structures in this area can be other causes of subsidence. The obtained results showed that the Goldstein filter has subsidence values ​​up to 9 cm in certain ranges and the uplift values ​​up to about 8. 5 cm and the Adaptive filter subsidence has values ​​up to 9. 5 cm in the range and the values ​​of the uplift up to about 8 cm. The reason for the difference in values ​​ is the results of these two filters. The Goldstein filter, by manipulating the phases, increases the coherence and the brightness of different parts and is higher and brighter, so the situation is better in this filter. In Adaptive filter, phases are not manipulated, coherence zones remain and the amount of blur is greater in different parts of the image. However, in each picture, the amount of subsidence is significantly observed in the east of the area, which is due to the greater concentration of agricultural activity and the use of groundwater.

There is a close relationship between geological formation and subsidence: the weaker the formation, the more noticeable the subsidence in the area. As can be seen, the subsidence in the alluvial formation is more than the volcanic conglomerate formation and the reason is the hardness of the conglomerate stone. As a result, alluvial rocks, andesite and marl have the most subsidence and latite (volcanic) have the least subsidence. Given that the hypothesis of this research was based on the principle that increasing the amount of groundwater extraction causes subsidence in the region, the relationship between the depth of wells in the area and the amount of subsidence in that area can be a good indicator to assess the accuracy of operations. Therefore, the statistics of piezometric wells in the region have been received from the Regional Water Organization of East Azerbaijan Province. After converting it to Arch GJS software database format, the relationship between subsidence values and piezometric well depths was established using zonal spatial analysis functions. Regression correlation analysis between these two factors showed a positive correlation of 87% between them and the implication is that any place that has a lot of subsidence in it is deeper piezometric wells. This finding confirms the hypothesis of this research that there is a direct and strong relationship between subsidence and groundwater abstraction.

The use of radar interference method in this study introduces a good capacity for its capabilities in determining the amount of subsidence in the study area. Findings from the use of this method showed a relatively high rate of subsidence for about a year. According to the maps, the maximum and minimum subsidence in the mentioned periods are 9.8, 0.6 cm, respectively. After entrophagram processing, time series analysis method with least squares method was used to determine the average  subsidence rate in the time period of the images. Based on this analysis, the average subsidence rate is equal to 1.2 cm. One of the causes of subsidence in this area is the indiscriminate use of groundwater resources and reduced rainfall, which has led to a drop in groundwater levels. In the present study, after initial processing on Sentinel-1 satellite data in remote sensing software and GIS, the amount of subsidence in this plain was estimated. Also in this study, both Goldstein and Adaptive filters were used for conducting further investigation. The obtained results indicated that the Goldstein filter has subsidence values up to 9 cm in certain ranges and the uplift values up to about 8.5 cm and the Adaptive filter has the subsidence values up to 9.5 cm in some ranges and uplift values are also shown up to about 8 cm. The reason for the difference in values in the results of these two filters is that the Goldstein filter, by manipulating the phases, increases the coherence rate and the brightness of different parts and the image is brighter, so the situation in this filter improves. But this is not the case with the Adaptive filter, and the phases are not manipulated, and in some areas the coherence remains, and the amount of blur is greater in different parts of the image. However, in both images, the amount of subsidence can be seen significantly to the east of the area, which is due to the greater concentration of agricultural activity and the use of groundwater.