فهرست مطالب eisa jokar sarhangi

Soil erosion is very obvious and noticeable in Iran, where a large part of it is a desert and the soil does not have a suitable cover. In order to reduce soil erosion and control it, it is necessary to identify factors affecting soil erosion. Land exploitation methods, forest and pasture exploitation, creation of residential and urban areas, geological conditions, precipitation, weather factors, etc. are some of the factors that affect the intensity of erosion in the region. Therefore, knowing the quantitative values of soil erosion is effective in accurately estimating the adverse, hidden, and intangible effects of erosion

In this research, the Revised Universal Soil Loss Equation (RUSLE) Model has been used. The amount of soil erosion in the basin was calculated in two 20-year periods (1980-2000 and 2000-2020). This soil erosion model is calculated through six factorsA=R×K×L×S×C×P      (R) the Rainfall erosivity factor, (k) soil erodibility factor, (LS) are slope length and slope degree factors respectively, (c) vegetation management factor, and (P) soil protection factor. The data in this research for the implementation of this model including the rainfall erosivity factor (R), topography including slope length and slope degree (LS), and vegetation management (C) were prepared from the Google Earth Engine system. The soil erodibility factor (K) was obtained from the natural resources report. The BLM model was also used to estimate the evaluation of the RUSLE model.  The Google Earth Engine system was used to prepare the land use maps of each of the 20-year periods. The land use map of the first period was prepared using the supervised classification method and calling Landsat 5 images, and The land use map of the second period was done using the CGLS-LC100 product, which is produced and updated at the Copernicus Global Earth Center and using Sentinel satellite images

By analyzing the obtained maps, the rain erosion factor for the first period (1980-2000) has an average of 2.02, the minimum value is 1.80 and the maximum is 2.29, and the second period (2000-2020) is from 1.55 to 2.07 is variable, its average is 1.783. The erodibility factor of the region's soils varies from 0 to 0.349 and its average value is 0.0264. The topography factor of the studied basin varies from 0 to 250 and its average value is 8.77. The vegetation management factor varies between 0.181 and 0.505 and its average is 0.353. This factor varies between 0.315 and 0.494 in the second period, and its average is 0.429 and has an increasing trend compared to the first period, which indicates the decrease of vegetation in the study area compared to the first period. The ground protection factor is also considered to be 1. To calculate the annual average soil erosion rate of the Award Basin in the first period (1980-2000), the various factors of the RUSLE model were converted to the same raster format and cell size, and coordinate system. To determine the risk of soil erosion, the produced layers including the rainfall erosivity layers, soil erodibility, topography, vegetation and soil protection factor were multiplied with the help of the Spatial Analyst extension of the ArcGIS program. The amount of annual loss of soil in terms of (tons per hectare per year) was obtained on a cell-by-cell basis. According to the results, the amount of erosion in the first period varies from 0 to 59.62 tons per hectare and its average is 1.64. The highest amount of soil erosion is in the middle and eastern regions with high slopes. In the second period, the layers of the soil erodibility factor, topography and soil protection factor are the same as in the first period because they are considered constant. The amount of erosion in the area in the second 20-year period varies from 0 to 63.38 tons per hectare, and its average is 1.75 and shows an increasing trend compared to the first period. The RMSE, MAE and MSE statistical indices were used to evaluate the accuracy of the RUSLE model and the degree of agreement of its erosion classes with the output of the BLM based on the map of sampling points. Examination of the values of the mentioned indicators showed that the root mean square error, mean absolute error and mean square error statistics in the RUSLE model have low values of 1.23, 0.96 and 1.52, respectively, as a result this model has It is a small error. After preparing the land use map for the first period, it was determined that in this period, the studied basin includes four land uses: forest, agriculture, pastures and woodlands, and residential. In the second period, there have been forest uses, destroyed forests, agriculture, pastures and woodlands, and residential areas. Then the land use maps prepared in the Google Earth Engine in two periods were transferred to the GIS environment system and the area of each of the above uses was obtained. The area of forests in the studied basin has decreased by 1590.956 hectares and 2014.827 hectares have been added to the area of degraded forests. Also, 2128/368 hectares have been added to the area of agriculture in this basin, and 2557/865 hectares have also decreased from the area of pastures in this area. It should be noted that 5.6266 hectares have been added to the area of residential areas of this basin.Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text.

The findings of this research in two 20-year periods (1980 2000) and (2000 2020) in the studied area show that the amount of precipitation in the second period has decreased compared to the first period. The amount of Rainfall erosivity in the first period had a minimum value of 1.80 and a maximum value of 2.29. In the second period, its value varied between 1.55 and 2.07. Thus, the amount of precipitation in the second period did not greatly affect the process of increasing soil erosion. However, the examination of the pictures and maps prepared on the amount of land use changes shows that the amount of forest and pasture area in this region has decreased and the area of destroyed forests and agricultural and residential lands has been added. Therefore, the role of land use changes in increasing soil erosion is significant. The research results of Teimouri (2018), Mayahi (2021) and Arkhi (2022) also confirm that increasing rainfall, changing land use and reducing vegetation are effective in increasing soil erosion.

The studied area on the Haraz Road from Poldakhter to Vana is prone to avalanches due to its cold mountain climate. The purpose of this research is to determine the most important factors affecting the occurrence of avalanches and its risk zoning using LNRF and Shannon Entropy models. For this purpose, the criteria of snow cover, elevation, slope, aspect, slope curvature, landuse and distance from the road have been used. The results of determining the most important factors affecting the occurrence of avalanches in the studied area using Shannon's Entropy model showed that the aspect, landuse, distance from the road and snow cover with weights of 0.541, 0.143, 0.129 and 0.083 respectively are more important. Avalanche risk zoning maps show that the highest avalanche risk is due to the location of these areas at an altitude of 2100-2700 meters and a slope of 30-60 degrees with the direction of the northeastern and northern slopes. Evaluation of avalanche risk zoning maps using the empirical probability index (P) indicates the appropriateness of Shannon entropy and LNRF models in the region, but the application of Shannon entropy has led to an increase in map accuracy up to 97%.

Among the various forms of water erosion, badland erosion is one of the most important emerging factors in water resources and its sustainability. The purpose of this study is to zoning of e badland erosion in the kelerz Basin in Garmsar. For this purpose environmental factors including geological formations, elevation classes, slope, slope direction and vegetation were compared using pairwise comparisons. Also, pairwise comparisons between sub-basins were performed in each of the five criteria mentioned above, and by calculating the multiplication of the total weight of the criteria and sub-basins relative to each other, the priority of each sub-basin was determined. In parallel with the above steps, the area distribution map was mapped using Landsat 8 OLI satellite imagery and Google Earth imagery and was monitored and modified with the help of field surveys and operations to eventually overlap sub-basin priority maps with the existing erosion status map. The accuracy of the proposed model was evaluated. The results of the overlap of the badland Distribution layer with the Erosion Intensity Layer show that sub-basins 2, 3, 4 and 12 have a 100% correlation between the rank calculated in the study with the rank obtained from the frequency ratio. Also sub-basins 1, 5, 9, 10 and 11 with amplitude difference of maximum 2 rank imply high accuracy of calculations. The results also show that sub-basins 12 and 9 have the highest intensity of badland erosion. The sub-basins 11, 4, 6 and 7 are characterized by high erosion and sub-basin 10 with moderate erosion and the rest of the sub-basins are evaluated with low erosion.

Evaluation and zoning of soil erosion using models that are more accurate, helps to implement conservation activities and control soil erosion within the watershed and reduce the amount of sediment outside it. The aim of this study was to zoning the soil erosion intensity of Baladeh watershed using RUSLE and ICONA models and toa investigate their accuracy with observations and terrestrial reality values. RUSLE model factors including rainfall erosion (R), soil erodibility (K), topography (LS), vegetation (C) and conservation operations (P) were calculated from rainfall data, soil map, digital elevation model and remote sensing techniques, respectively. In ICONA model, soil erodibility map was obtained by combining two layers of slope and rock surfaces. Then, in order to prepare soil conservation layer, vegetation index and land use layers of the area were overlapped and by combining erosion and soil conservation layers, erosion zoning map was prepared with this model. The accuracy of these models was evaluated using statistical indices and BLM method, all of which were obtained during field observations. For this purpose, a map with 2500 points was prepared as regular networking for sampling the maps obtained from the models and accordingly, RMSE statistics (root mean squares error), MAE (mean absolute error), MSE (mean squares error) and NSEC (Nash-Sutcliffe Model Efficiency Coefficient) were calculated. The results of the mentioned statistical indices and the errors of the models shows that the mentioned models are not sufficient in baladeh watershed, but the efficiency and degree of adaptation of the erosion classes of ICONA model with BLM output as reference map is higher, because the amount of RMSE, MAE and MSE is lower and NSEC is closer to number one.

Different forms of water erosion can be observed in many parts of Iran, and the specific role of each factor in sediment delivery can be presented. This research was conducted to investigate the effect of geological formations and topographic features on creating important forms of water erosion in the Golidagh watershed in the Golestan Province.

At first, we used the Google Earth images, aerial photos, maps, and field surveys of the area, so that the forms of erosion and their quality were determined. Lithology, elevation, slope, and aspect were selected as independent variables, and each form of water erosion, including sheet erosion, rills, gullies, and badlands, were determined as dependent variables. After, independent variables information layers were overlay with a layer of erosion forms in the ArcGIS and the relationship between them was obtained through Chi-square (ϰ2 ) distribution using SPSS software.

Overlay analysis of the maps showed that the advanced forms of water erosion, including gully erosion and badlands, are dominant as the loess formations in altitudes less than 800 meters, and were more than expected. Also, an area of all forms of erosion is low at the slope of 0-10 percent and their area increases at the slope of 10 percent and higher. investigation of forms of water erosion in relation with the aspect showed that advanced erosion of the gully and badland had more in southwest area and were more than expected, while the surface lacks erosion predominates in the north, northwest and northeast areas. The relationship between variables with different forms of erosion in the area was determined through the chi-square test. The results showed that the relationship between the studied variables and forms of water erosion in the area is significant at 1%. The intensity of the relationship based on the contingency coefficient indicates the more importance of elevation (0.631) and geological formations (0.461) in the region.

The created forms of erosion are dependent on elevation and lithology in the area, and these variables can be used in selecting appropriate methods and executive strategies for managing forms of erosion and controlling their intensity.

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.

Karst aquifers in semi-arid regions play a vital role in people's lives. Therefore, recognizing the pollution potential of aquifers and their vulnerability in different sectors is very important. The western region of Iran, due to the expansion of the Zagros carbonate mountain range, has numerous karstic aquifers that are exposed to pollution in terms of environmental conditions and extensive human activities. The purpose of this study is to estimate the vulnerability map of Aleshtar plain karst aquifer located in Lorestan province against COP pollution. This model evaluates the vulnerability of karst water sources to pollution using three factors, cover layer (O), flow concentration (C) and precipitation regime (P). The results show that 19.14, 28.76, 35.01 and 17.09% of the area are located in very low, low, medium and high vulnerability zones, respectively, which indicates the almost low vulnerability of this karst aquifer compared to It is pollution. Findings indicate that the importance of the overlay factor, precipitation regime factor and flow concentration factor in the pollution of Aleshtar aquifer, respectively. Due to the small area of ​​fully developed karsts and the high slope of areas with developed karst, the role of flow concentration factor in pollution of Aleshtar aquifer is less and due to the drought trend of recent decades and the change of precipitation from snow to rain showers that water retention and therefore less penetration In the karst basin of Alshtar, the importance of the rainfall regime in the vulnerability of the Alshtar aquifer has decreased. Finally, due to the high permeability of the alluvial plain of Aleshtar, the cover layer has provided the ground for aquifer pollution. In general, in the study area, C, P and O, respectively, have the most role in the vulnerability of the area.

Forms of erosion demonstrate its intensity. The study area is one of the suitable rangelands for animal husbandries in the Province of Semnan, which is subjected to a plethora of water erosion forms due to an overuse of the land. Therefore, it is necessary to investigate the effective factors in an aggravation of water erosion conditions in this area to manage and control it. Erosion in arid and semi-arid regions is intense; therefore, it is necessary to take action to inhibit or reduce it. As the forms of erosion are identifiable, we tried to determine the relationships between geomorphologyand forms of water erosion, namely: sheet, rill, gully and badland in a sub-watershed of the Hablehroodcatchment. Different classes of lithology, slope, aspect and elevation were correlated with any of forms of erosion in the area to determine their variety in the development and dominant erosion on each of the classes and each factor. Using the guidelines of the Land Degradation Assessment in Drylands (LADA), the forms of erosion in terms of its intensity was ranked and was used according to the typology of the variables. The Kruskal-Wallis test was on. The results indicated that the highly developed forms of water erosion, gullies and badlands, marl, gypsum and salt formations (8.4 and 15.8%), an elevation of 1000-1200 meters, the slope of 10-20% (8.7 and 15%) and the southern aspects domain (5.7 and 9.5%) respectively. The result of Kruskal-Wallis test for the slope and aspect at 0.01 and elevation at 0.05, respectively, were found to be significant, which indicated that there were specified differences in the forms of water erosion at the specified classes of geomorphologyfactors of the area. Therefore, geomorphologyfactors may be used to assess forms of water erosion and its intensity in similar regions.

It is important to determine the ecological capacity of zones for appropriate location of urban places. In zonation these are different factor. The study attempts prioritize these factors and to develop the zonation map of the province of Mazandaran using models of ecological urban development, information value and correlation.

At first, effective ecological factors in determining appropriate zonation for urban development in the form of information layers were developed in Arc GIS environment the urban development ecological model was used for determining range of each class. However for calculating the weight of classes in each factor model of information value was used through comparing areas of cities in each class with the whole area. The weight of factors effective on zonation was calculated using the appropriate weight for each layer and implementing the above mentioned models.

Results show that elevation of 0-400 and 1200-1800 meters, slope of 0-6 percents, the northern direction, quaternary fluvial, precipitation of less than 500 and higher 800 millimeters, wet of less than 60 percents and vegetation density of 30 -60 percents are appropriate for urban development.

factors of soil, lithology, elevation, slope, vegetation, wet, rain and aspect received the highest to the lowest priority in location of urban areas and obtaining zonation map respectively. Evaluation of the models indicated the information value is of a higher accuracy.

One of the most important and advanced forms of water erosion is gully erosion that causes the destruction of land and sediment production in many areas, resulting in a lot of economic damage. The Nemarestagh watershed is one of the sub-basins of the river Haraz which is exposed to expanding this form of erosion. It is necessary to determine the sensitivity of the area by using appropriate methods to plan its control. For this purpose, 133 gullies were first recognized and recorded through Google Earth image, aerial photography and extensive field surveys. Randomly out of this number, 99 gullies are used for modeling and 34 gullies are implemented for evaluation. The layers of effective factors including lithology, elevation, slope, aspect and vegetation and land use were prepared using ArcGIS environment. In the next step, the length and density of gullies in per class of the factors were calculated by overlaying the layers and the map of the risk zoning of gully erosion in the region was provided using the frequency ratio and information value statistical methods. The results showed that Shemshak formation with shale, sandstone, siltstone and conglomerate, elevation of 1000-1200 meters, the slope of 40-50 percents, the southern direction and poor pasture have a highest gully erosion susceptibility. The evaluation of statistical methods using the probabilistic probability showed that the information value model is more suitable for hazard zonation in the area.

The quantitative analys is in geomorphology is very important. By this study we can determine the situation of a watershed in regard to erosion or to which a channel loses its balance and priority watershed management. The case study was done in Lar subwatershed in the central Alborz in order to investigate and compare methods such as those of Straller, Shidgar, Shrev and Horton. At first, the quantitative values of the geomorphologic parameters were achieved by using GIS. Then the relation between the orders of channels with different variables such as the length of channels, numbers of branches, the basin area, the slope of channel and drainage density were studied by the statistical methods. The curve of erosion cycle has shown that this area is in its young stage. Therefore. Using this method deterioration and erosion is dominant. The results also show that Staller method is the best model for ranking channels and determining some of the morphometric parameters. The shrev method is more effective in regard to ordering of the area zones.