فصلنامه پژوهش های فرسایش محیطی
سال دوازدهم شماره 3 (پیاپی 47، پاییز 1401)

Desertification is a type of land degradation that often occurs in semi-humid, semi- arid, arid or hyper-arid areas. It becomes drier and loses its original structure, water, plants, and wildlife as a result of a variety of factors such as climate change, soil over-exploitation, and human activities.Common to all definitions of desertification is the severe destruction of the environment and the reduction of biological production of ecosystems due to desertification. Desertification of arid and semi-arid ecosystems is one of the most critical issues studied in ecosystems, which have severe economic and ecological impacts on a wide range of geographical areas with the potential risk of desertification. Desertification has many consequences that directly and indirectly affect human life, the most important of which are mass migration, poverty, water and food shortages, and conflicts over land and water resources. According to studies by the International Fund for Agriculture, desertification threatens 40 percent of the planet and directly affects 12 million hectares of land annually. Different regions have different potentials for desertification development depending on their hydro-climatic conditions. Iran's hydro-climatic situation has caused many parts of it, especially the eastern regions of the country, to be prone to desertification. Considering the direct effects of desertification on human life and its important environmental effects, in this study, vulnerable areas against desertification in Kerman province have been studied.

In this study, in order to investigate the vulnerability of the study area, the DVI vulnerability index has been used. The information used in the research includes climatic information, a digital model of 30 m altitude, information about the type of soil in the area, and also some demographic information. The most important tool used in research has been ArcGIS. This research has been done in several stages. In the first step, the required information is collected. In the second step, in order to identify vulnerable areas, initially, the intra-layer weight of each parameter is determined and, subsequently, based on that, vulnerable areas are identified. In the third stage, using the DVI relationship and in ArcGIS software, a map of vulnerable areas against desertification has been prepared and then the status of desertification potential in Kerman cities has been evaluated.

In this study, in order to identify the vulnerable areas of desertification, factors including climatic indicators, topographic indicators, soil characteristics and also demographic indicators have been used. Climatic indicators are one of the effective factors in determining the vulnerability of desert areas, so that areas with less rainfall, high temperature and drought index and more evaporation have a high potential for vulnerability. Soil status and slope are also effective factors in aggravating the vulnerability of areas. Land slope is one of the effective factors in permeability, erosion and runoff. Typically, steep areas have greater potential for vulnerability. Sand fields and salt marshes are also prone to desertification. Demographic indicators are also effective factors in desertification. Demographic indicators used in this study include population density, population growth and illiteracy. Typically, areas with high population density, high illiteracy rates, and high growth rates have greater potential for vulnerability and are prone to desertification.

In this study, using DVI vulnerability index, the extent of desertification has been investigated in Kerman province. The results of the research indicate that a large part of the area of ​​Kerman province is covered by areas with high and very high vulnerability, so that 61722 square kilometers (equivalent to 34.7%) of the area of ​​the province is a class with high vulnerability potential and 32381 square kilometers (equivalent to 18.2%) of the province's area is covered by floors with very high vulnerability potential. The study of spatial distribution of vulnerable areas indicates that the southern regions of Kerman province have the highest potential for vulnerability due to climatic and demographic conditions. In this study, the level of vulnerability in different cities has been evaluated. Based on the results, the cities of Manojan, Qaleh Ganj and Anbarabad have the highest potential of vulnerability with 97, 96 and 91% of the areas, respectively. The results of the study also indicate that Kerman province has a high potential for vulnerability and the southern regions of the province are at risk of erosion, so it is necessary to plan for conducting measures to prevent desertification, especially in the southern regions of the province.

Qeshm Island is located on the southern coast of Iran in the Persian Gulf and the Strait of Hormuz, whose shores, like other coastal environments, are affected by processes and morphological changes due to sea hydrodynamics and geohydrology of the island's land environment. Mangrove forests on the north coast of Qeshm Island are a gentle environment for sedimentation of Qeshm Island runoff and sedimentation of rivers that enter this environment from the mainland. Also, along the coastline of the Strait of Hormuz and the Persian Gulf, there is the highest tidal range. Under these conditions, monitoring and digitization of temporal-spatial changes of Qeshm Island coastline can be important in sustainable coastal development and coastal strip integrity management, and by detecting and predicting it, a comprehensive plan for changes can be designed depicting developed morphodynamics, and sea retreat and forward patterns. Therefore, the present study, focusing on the coastal environment development approach, tries to classify coastal landforms and monitor the spatial changes of the coastline of Qeshm Island, which is analyzed through the Shepard classification method and the coastline system analysis tool (DSAS).

In this study, in order to classify landforms and analyze the shoreline changes of Qeshm Island based on integrated shoreline management, first by using library resources, analytical tools and field visits and by applying Shepard method, the coastal areas of Qeshm Island were classified into different zones. The data and tools used included topographic maps, geology and land use of the island, digital data and elevation model, and LANDSAT satellite imagery of the ETM + and OLI sensor series. For spatial analysis and drawing maps, ArcGIS software was used. After increasing the recovery and contrast power of water and land in ENVI software, the satellite images were transferred to Arc map software and from the images, Qeshm Island coastline polyline related to each image were drawn and the shoreline was prepared as the required layer. In this way, shorelines were prepared to monitor spatio-temporal changes with the DSAS tool. In order to statistically analyze and quantify the trend of backward and forward (moving) shorelines, statistical methods including endpoint (EPR), linear regression (LRR), weighted linear regression (WLR) and final confidence interval (NSM) embedded in DSAS tools were used in this study.

By studying the geological and topographic maps (Landsat 8 (OLI)) of the study area which was used to classify the coastline of Qeshm Island, five different morphological units including slopes, marine sediments, residential areas (man-made), terraces and the high hills in the area were identified and classified. The linear regression index (LRR) was obtained by fitting the least squares of the regression line to all points at a 95% confidence level in a particular transect, in which a positive value indicates coastal sedimentation and a negative value indicates coastal erosion. In the morphological units of the primary coasts of Qeshm Island, the highest advancement of the coastline has been in the high hills, which often ends in the hills of Basaeido port. It has flowed towards the shore and has caused sedimentation in the coastline, and its average has increased by about 11.3 meters per year during 31 years. Of course, in the coastline of Namakdan Cave Mountain, due to the lack of runoff and also the direction of the prevailing south winds, the waves have caused the coast to regress and erode; this area is located in the morphological unit of high hills, which is at least -6.3 meters. Therefore, among the morphological units of the high coastal hills, in Laseido, the LRR index is positive and indicates the advancement of the coast and sedimentation, but in the salt cave, the LRR index is negative and indicates the regression and erosion of the coast. The lowest LRR index among the primary beaches is in the morphological unit of coastal terraces (5.4 m per year) with a maximum sedimentation rate of 34 m/year in Goran in the west of the mangrove forests and a minimum of 27 meters per year in southern parts of the the island as well as the port of Laft. On Qeshm Island, the shores of mangrove forests, Dolab port, Dastko, Direstan and eastern man-made shores are considered as parts of the primary beaches. Among these morphological units, the highest rate of shoreline movement per year is LRR and EPR occurring on the shoreline of the mangrove forests, which are positive indicators and show that due to the massive sedimentation in this forest, the coast is progressing and on average annually between 21.7 up to 23.1 meters on the beach is added. The maximum of this positive displacement is about 76.6 meters per year and the minimum is 3.3 meters per year.

The surface runoff of Qeshm Island, which originates from the hills and terraces and reaches the shore, loses its initial energy by decreasing the slope and, in contact with the seawater, causes the deposition of its sedimentary load. Accumulation of these sediments has caused the shore (land) to advance towards the sea. In the mangrove forests of the north of Qeshm Island, the northeastern runoff of the island as well as marine sediments from the rivers of the mainland of Iran, especially the sediments of the Mehran River, have caused the shoreline to advance. Also, in Dolab, Basaido and Direstan ports, sediments from runoffs have caused the coastline of Qeshm Island to advance towards the sea. But on the southern coast of Qeshm Island, due to waves and winds from the west and southwest, we see regressive erosion of the coastline and the coastline recedes to the mainland by at least 27 meters annually. The results of the present study can be used in Qeshm Island coastal strip integrity management plans and experts must be notified that strong waves cause erosion of the south coast of Qeshm Island, but sedimentation of the resulting runoffs can advance the coastline in the north of Qeshm Island.

Floods and their consequences, with the intensification of human exploitation of nature in the early twentieth century, have had negative effects on vital ecosystems. Also, adverse effects of erosion, while destroying the harvest site, lead to reduced production capacity and degradation of physical and chemical properties of soil in lands. To manage this phenomenon, the factors of production and flood must be identified and then areas with high potential in flood production must be identified to enable the possibility of executive and corrective operations at smaller and risky levels. The level of flood areas in the country is estimated at 91 million hectares, of which about 42 million hectares have moderate to very high flood intensity. Therefore, knowing the flood situation of the regions is a necessity to prepare strategic plans for sustainable management of basins. The purpose of this study is to estimate flood potential and determine the priority of flood areas by physical factors to combat erosion in Hajilar watershed by using a combination of data and information based on the CN-SCS method and also to determine critical flood areas.

In this study, in order to investigate the potential and zoning of flood risk in the basin, at first, by studying and examining the foundations and theoretical background of the subject, the physical factors affecting the occurrence of floods were identified.Then, the required information was collected and layers of each of the proposed factors were prepared in Arc GIS 10.7 software and Arc-Hydro and Arc CN-Runoff extensions. In this regard, the information layers of the waterway network, level lines, and elevation classes were prepared using a digital elevation model with a scale of 1: 120,000. Lithological information layers were obtained using the geological maps of Siah Rud, Tabriz, belonging to the Geological Survey of Iran. The precipitation map of the basin was prepared using data from meteorological stations within the study area and also adjacent stations using IDW interpolation method. Land use layers of the area were obtained using the area land use map and monitoring using Sentinel 2 satellite imagery. The soil map of the region has been prepared using studies of natural resources of Arasbaran basin prepared by the Forests, Rangelands and Watershed Management Organization of the country and controlled with lithological conditions and other environmental factors. Vegetation of the area was prepared through NDVI index. Then, by extracting the number of curves (CN) and the amount of penetration (S), the layers were combined and the runoff height of the basin was estimated by the runoff curve number (CN-SCS) method, the units were classified by SPSS software and the priority of areas in terms of floods in the basin was determined. In order to determine the peak discharge of flood through the obtained data, first based on the proposed Schwab relation, the water accumulation time of all sub-basins was prepared separately and, finally, the maximum peak discharge obtained from this runoff was obtained.

The results of the SCS-CN model which was intended to determine areas with different flood potential in the basin and compare it with environmental factors such as slope, lithology, land use, rainfall, etc in the basin indicate that this model is highly capable of estimating the flood potential in different areas of the basin. Results of changes in the number of curves based on effective environmental factors were also presented in Table 1.The potential for runoff production was high in high altitudes with poor pasture landuse or dryland agriculture with poor permeability soil, as well as in residential areas where the city surface consists of impermeable or low permeability surfaces.Also, there was a high flood potential in the  dense and medium pastures with hydrological group B. Irrigated and rainfed agriculture with hydrological group A has the lowest runoff potential and thus has the lowest amount of flooding in the basin.By obtaining the runoff height in different parts of the basin and determining the levels of changes in the values through quartering, it was found that mainly the central and lower parts of the basin are in the first priorities in terms of flood potential. Poor coverage, low permeability, and low rainfall in this area are some of the factors that increase flood potential. The southern and central regions are areas marked by low sensitivity to flooding. By prioritizing areas in terms of floods and mapping them, the results can be used in watershed management operations at the level of high-sensitivity units to reduce erosion and damage. Taking into account the values of runoff height relative to the sub-basins and water accumulation time obtained based on the Schwab estimation method, the results showed that the flood volume and maximum peak discharge in the basins have a good relationship with each other and the highest maximum peak discharge is related to H33 and H18 sub-basins with volumes of 51.44 and 48.67 and the lowest is related to H12 and H29 sub-basins with volumes of 3.77 and 3.86 m3/s.

Floods are among the most important environmental hazards that cause human and financial losses every year. In the meantime, using the SCS-CN experimental model, as a method for estimating floods in basins with different environmental conditions, and the inter-environmental approach in it can be a useful solution in watershed management studies. Considering that human intervention has caused an increase in floods in all areas, providing methods for accurate flood estimation is one of the basic needs of the relevant responsible organizations. Accordingly, the present study was conducted to determine the priority of areas in terms of flood potential and the results showed that 9% and 51% of the basin areas are in the very dangerous and high-risk flood categories, respectively. According to the final map obtained, Areas with very high risk and high risk are mainly located in residential areas and in the lower areas of the basin, which are the first priority in programs related to water resources, especially flood control and watershed management in the upstream units. Also, according to the results, areas with medium risk potential occupy 23% of the area and 17% of the area has low risk potential. The results of the study confirmed the high potential of the studied area in terms of flood risk, so lands with very high and high risk are lands that should be protected and appropriate watershed management measures must be conducted to control the speed of floods and reduce soil erosion.

Severe soil erosion is a serious threat to the sustainable management of land and the use of water and soil resources in many parts of the world. In order to control erosion of sheet, rill, gully, and stream bank erosions and to reduce the resulting sediment at the outlet of watersheds, it is necessary to identify the share of sources that produce their sediment to make protective measures more successful. One of the most common methods that has been used in recent years to determine the share of different sources of sediment is the sediment fingerprinting method.

The purpose of this study is to investigate contribution of sheet, rill, gully and stream bank erosions in sediment production by using sediment fingerprinting method in Neyriz watershed, located in East of Fars province, with the help of sampling of sediment deposited in the bed. From each type of sediments, sheet, rill, gully and stream bank erosions, the main waterway within the basin and the outlet area of ​​the watershed, 10 samples (60 samples in total) were collected. In order to determine the optimal tracers, two tests of "domain" test and "multivariate detection analysis" were used. Furthermore, by using the model of Collins et al., the share of each of the different sources of sediment was obtained. Then, the uncertainty related to the share of potential sources of sediments was calculated using the Monte Carlo simulation method with 95% confidence in MATLAB software. In order to evaluate the results of the hybrid multivariate model, the Goodness of Fit (GOF) proposed by Collins et al. was used.

Based on the range test, among the 51 tracers measured in the samples, twelve tracers (Ag, Ba, Be, Eu, Mn, Ni, Ta, Tb, Th, Tm, W, and Zn) are found as tracer’s non-conservative variables were identified, and these detectors were discarded in other statistical tests such as Kruskal-Wallis and discriminant analysis function. The results of the Kruskal-Wallis test showed that among the 39 tracers that passed the range test, sixteen tracers (Al, Ca, Co, Cr, Er, Fe, Gd, Lu, Mo, Na, Nd, Pb, Pr, S , Sc and Zr) with significance at one percent level (p ≤ 0.01), and 9 tracers (Cu, Ga, Hf, Ho, La, Sn, Sr, Y and Yb) with significance at five percent level (p ≤ 0.05) is that in total, these 25 detectors had a significant level and could separate sources; while fourteen tracers (As, Ce, Cs, Dy, K, Li, Mg, Nb, P, Rb, Sm, Te, Ti and V) were not statistically significant, these tracers were deleted from the DFA statistical test. In the first step of the DFA test, the Zr detector, the second step of Zr and Al detectors (with Wilkes lambda from 0.717 to 0.244), the third step of Al, Zr and Fe detectors (with Wilks lambda from 0.39 to 0.057), the fourth step of Zr, Al, Fe and Sn detectors (with Wilkes-lambda 0.362 to 0.04), the fifth step of Zr, Al, Fe, Sn and Lu detectors (with Wilkes-lambda 0.233 to 0.03) and the sixth step Zr, Al, Sn and Lu tracers (with Wilks lambda 0.289 to 0.045) were entered into the model. Based on the obtained results, among the 25 tracers that passed the Kruskal-Wallis test, five tracers (Al, Fe, Lu, Sn and Zr) were entered into the DFA test step by step. In the third stage, iron tracer (Fe) was added to the model and in the sixth stage, it was removed from the DFA test. In general, four Zr, Al, Sn and Lu tracers were selected as the final optimum tracers. These four detectors were able to correctly classify 95% of sediment sources. The findings of this research, which were obtained by using Monte Carlo simulation and the combined multivariable model and evaluating their results using GOF, showed the contribution of gully, sheet, rill and stream bank erosion to the order is equal to 45.21, 3.07, 16 and 35.72% of the total erosions that have occurred in this watershed. Also, considering the GOF value of 0.8869 and mentioning that the closer this value is to one, the more accurate the results of the model is true in this research and this analysis also confirms the high accuracy of the model.

In this study, the efficiency of sediment fingerprinting method was proved as a successful and effective method to determine sediment sources because the first and most important stage of the sediment source method is to choose a suitable combination of tracers that can isolate sediment sources, and this was done correctly in this research. Also, Monte Carlo uncertainty confidence levels showed that the scope of this uncertainty is large (0.8869) and therefore, it shows a greater lack of certainty on different sources of sediment production. Determining the share of four types of erosion in the Neyriz watershed and placing the share of gully erosion as the most important type of erosion in the production of productive sediments in it shows the importance of controlling erosions, especially gully erosion, with emphasis on biological plans.

Nowadays, an unprecedented need is felt for considering groundwater quantity and quality in water resource management plans in all ecosystems. The availability of adequate and proper water resources for various uses is an important factor of sustainable development in arid and semi-arid regions such as Minab Plain in the south of Iran. Given the climatic conditions of this plain and the overuse of groundwater tables for various uses, it was decided to investigate the variations in the quantity and quality of groundwater tables and simulate their future trend to find out their impact on the rate of desertification and land degradation. There are diverse traditional methods to assess the quantity and quality of underground water, but they are not economical in time and cost. Today, modern groundwater quantity and quality determination methods have mostly resolved this problem to be informative about the future trend of these variations. Hydrological models were used to estimate groundwater variations trends. Different scenarios were applied to plan and manage these irreparable resources in the future. The present study evaluates groundwater tables' quantitative and qualitative variations using the MGS software package and the Modular Three-Dimensional Finite-Difference Groundwater Flow Model (MODFLOW) code.  The variations of these irreparable resources were simulated in Minab plain to study the rate of desertification in the past, present, and future. Finally, the managerial approaches were proposed to prevent the further depletion and quality loss of groundwater tables in this plain and adopt strategies tailored to determine the desertification rate in the future.

For this research, data of hydrology and geology of the Minab Plain aquifer were supplied by Iran Water Resources Management Company for 2003-2018. After data collection, GMS10.5 was employed to study and model groundwater resources and their future condition. First, the variations in groundwater quantitative and qualitative parameters in Minab Plain and their past, present, and future states were examined. New scenarios of use increase of 5%, 10%, and 15% higher use were used in GMS10.5. Then, the IMDPA model was employed to assess the rate of desertification of groundwater tables in the plain in the past, present, and future.

The results showed that the highest depletion was -3.16, -12.87, -23.89, and -30.30 in the base years of 2003, 2008, 2013, and 2018, respectively. It has happened due to the digging of deep and semi-deep wells. The highest increase in water level balance over 2003-2035 has been -59.5, -61.3, and -63.2 m under the scenarios of 5%, 10%, and 15% higher use, respectively. Prediction of the qualitative parameters of EC and SAR for 2019, 2024, 2029, and 2035 indicates that these parameters will increase in 2035. The resulting pollution will move from the south towards the north. The weighted mean of quantitative values of the factors influencing water resource degradation shows that desertification occurs in the studied region at different rates. Over time, the desertification rate of water criterion will expand towards the north, south, center, and west of the area. The results for different periods are depicted in a figure. It can be asserted that a great part of the region is in the low desertification class in the base period, but the area of this class decreases. The medium desertification class expands over time. The area of the medium desertification class has been 0, 32.37, 46.35, and 123,48 km2 km2 in 2003, 2008, 2013, and 2018, respectively. The very low desertification class has been 125.93, 6.54, 0.23, and 0.07 during this period. In the 5% higher use scenario, the very low, low, and medium desertification classes have decreased by 2.64, 45.47, and 72.04 km2 over 2009-2035.  The very high desertification class has increased by 120.14 km2 in this scenario. It was the same for the scenarios of 10% and 15% higher use. The very low, low, and medium desertification classes have decreased by 0.06, 72.18, and 48.00 km2 in the 10% higher use scenario and by 47.96, 0.06, and 72.14 km2 in the scenario of 15% higher use over 2019-2035. The very high desertification class has increased by 120.189 and 120.187 km2 in these scenarios, respectively.

The results revealed that desertification is happening across the plain at different rates and will continue at a higher rate in the future. It can be summarized that all components of nature are chained to one another. A change in one component entails a change in the other parts of the system so that a decline in groundwater quantity reduces its quality. So, approaches should be adopted to prevent the further quantitative and qualitative decline of groundwater tables, and mechanisms should be taken to minimize damages to the whole ecosystem. Finally, some executive approaches for preventing land degradation and desertification based on the regional water criterion include reforming cropping patterns, adopting modern irrigation methods, and feeding the bed of the plain by flooding.

Soil characteristics and development are influenced by environmental factors such as land use. Investigating the agricultural use effect on soil development and evolution can be useful in maintaining and improving the quality of land resources and preventing soil erosion and degradation. Soil development and evolution are the result of changes in its physical, chemical, biological, morphological, micromorphological and mineralogical characteristics, so the study of soil evolution indices indicates the impact of soil on environmental factors. Various morphological, physicochemical, and micromorphological indices of soil evolution due to their special nature show different aspects of soil impact from environmental factors. Therefore, the study of soil evolution as a result of environmental conditions of its formation and development can provide a suitable perspective for the continuation or the need to change the current management to maintain or improve land quality. This research study intends to examine the various indices of soil evolution that consider each aspect of soil development and evolution in relation to land management to be a fundamental step for the sustainable management of agricultural land.

The study area is the cultivated lands of alfalfa, corn, onion and wheat, and the adjacent barren lands in the Shurgol section of Bonab city of East Azerbaijan province, which is the site of selected control soil profiles under constant and uniform management over an average period of 15 years. After selection, the soil profiles were dug and described, and disturbed and undisturbed samples were taken from genetic horizons for physicochemical and micromorphological analyses and were transferred to the laboratory. Soils were identified and classified according to field studies and the results of laboratory analysis based on 12th edition of the Keys to Soil Taxonomy. Micromorphological studies of thin sections prepared from undisturbed soil samples were performed using a polarizing microscope based on a standard terminology system. In this study, a set of soil evolution indices, including harden, clay accumulation, appearance cation exchange capacity, MISODI, MISECA, and revised MISECA were used. Harden index is obtained by comparing the characteristics of texture class, plasticity, adhesion, type and degree of structure development, dry and wet stability of aggregates, clay coatings, darkening, lightening color and acidity of soil solum with C horizon. Clay aggregation and the apparent cation exchange capacity indices have been calculated based on the differences between the amount of soil clay in horizons B and C and the ratio of soil CEC to the percentage of clay, respectively.
To calculate the MISODI, MISECA and revised MISECA indices, the studied pedofeatures during micromorphological analyses, including microstructure, b- fabric, coating, nodule and degree of weathering and evolution of mineral particles were used. For this purpose, each of the mentioned characteristics, in terms of quality and quantity, was assigned a weight based on the relevant scoring tables, and the final evolution index was calculated from all of them.

The results showed that the soils of the region are non-saline and pH neutral; their texture is clay loam to clay and different amounts of organic carbon with irregular depth distribution are observed in soils under each type of productivity. Based on a complete study of the characteristics, different families of Inceptisols were identified with cambic and calcic horizons characteristic as well as Vertic properties in the soils of the region. Micromorphological studies revealed that the voids are simple and compound packing and include channels with granular and blocky microstructure in the surface horizons as well as chamber and sometimes vugh with massive microstructure in the lower horizons, while in the middle horizons angular blocky and subangular blocky microstructure were observed. According to the boundary between fine and coarse particles with 20 microns dimensions, the c/f particles in all soils have increased from surface to depth and the coarse part includes a variety of minerals. The b-fabric and related distribution pattern between the predominant coarse particles are of the crystalline and porphyric types, which in some cases are observed in speckled and monic forms, respectively. Pedofeatures observed in the soils of the region include intact, semi-decomposed and fully-decomposed organic residues, partial clay coatings and continuous and incomplete lime coatings and infillings in the channel. Soils under alfalfa productivity type with values ​​of 26.98, 1345.83, 13, 15 and 16 were identified as the most complete soils for harden, clay accumulation, MISODI, MISECA and revised MISECA soil evolution indices, respectively, while barren soils with 15.52, 419.75, 7,8 and 8 were identified as the least developed soils for the mentioned indices and the evolutionary order between these two groups is based on the mentioned indices for the productivity types of corn, onion and wheat. The values for the appearance cation exchange capacity index were 0.7 for wheat and barren soils, 0.68 for corn and onion and 0.67 for alfalfa.

As for soil classification, although differences exist at different categories or levels, but in this study, even at the family category or level, it is not possible to express the differences between soils under the productivity types of corn, onion, wheat and barren lands. In such cases, it can be useful to consider the soil evolution indices that quantitatively examine soil properties and determine their differences. Soil properties and, consequently, their evolution are affected by the productivity type in agricultural uses, and among these properties, the ones related to soil fabric are affected more than the others. Observing the evolutionary order of the majority of soils based on the studied indices for the productivity types of alfalfa, corn, onion, wheat and barren lands indicates that optimal agricultural operations and sustainable land use lead to accelerated evolution. Based on the quantitative values obtained for different indices in different types of productivity, it can be stated that the effectiveness of soils from the cultivated crop depends on the morphological and physiological characteristics of the plant and their planting, holding, and harvesting operations. Therefore, in arid and semi-arid areas, such as the studied area, where the soils are young and at the beginning of their evolution, the existence of more complete soils will be in line with increasing their quality and will prevent land degradation.

The Dust storm is one of the most important climatic phenomena of the present age in arid and semi-arid regions of the world. Due to its characteristics, this phenomenon can have various environmental and climatic effects in different atmospheric systems, oceans and continents. Dust storms play a very important role in the earth's atmospheric cycle, so that dust particles are one of the factors of atmospheric warming, as well as reducing air quality and affecting human health. Dust particles have direct effects on absorbing or scattering radiation waves, which results in a significant decrease in visibility. The west and the northwest of Iran are facing the dust storm phenomenon and its problems periodically.

In this study, the frequency of dust phenomenon and its occurrence causes have been analyzed in the northwest and west of Iran, including Ardabil, East Azerbaijan, West Azerbaijan, Kurdistan, Zanjan, Hamedan, Kermanshah and Ilam provinces. At First, the occurrence of dust in the study area was investigated. To identify the source of the dust storm, dust storms of 15-20 July 2000 & 17-22 June 2012 (as case study) were described using MODIS satellite images. Modis sensor images were used to detect and monitor the dust storms. The reason for choosing these days for analysis was the high intensity and concentration of dust in those days in most of the stations. For the synoptic analysis of dust storms, re-analyzed data of sea level pressure, geopotential height, vector wind, vorticity, wind direction and omega at 500mb level were used. These data were obtained from National Oceanic and Atmospheric Administration (NOAA) which was prepared by the National Center for Environmental Prediction (NECP). The maps were drawn and synoptically analyzed using Grads software.

The results showed that the highest frequency of dust storm codes occurred in the studied stations was related to code 6, which indicated the presence of dust in the sky. In terms of monthly distribution, June, July, April, and May has the dustiest days in the studied area. The occurrence of dust had an increasing trend from the early hours of the night to noon. The frequency of dusty days in different years indicated an increasing trend from 2000 to 2018. The frequency of dusty days was higher in 2008 and 2012 compared with other years. The results also showed that the main sources of dust in the west and northwest of Iran are the desert areas of Iraq, the Arabian Peninsula, and the African Sahara. At sea level, the most important cause of dust storms in the west and northwest of Iran was the existence of a low-pressure center in Saudi Arabia and Iraq. In other words, dust storms in the west of Iran were transitional and have originated from the western neighbors of Iran (Iraq, east of Syria, and northern Saudi Arabia), where in the most severe event, the dust has spread to central Iran. At 500 mb level, the most important factor in creating dust storms in the west and northwest of Iran was the rule of the Azores sub-tropical high-pressure tongue and the location of the region below it. Due to the high pressure of the Azores on Iran, migratory cyclones and troughs due to lack of penetration into the region, had little effect on the formation of instability and dust storms, which the main reason for this phenomenon should be found in surface pressure systems. The extreme warming of the earth's surface had caused the instability of the atmosphere of Iran in summer up to a height of 2-3 km above the earth's surface. The low-pressure thermal cell was stretched from Pakistan to the south of Iran, and from there to the deserts of Saudi Arabia, Iraq and Syria on all days under study. The Iran-Pakistan low-pressure system, which appeared as a focal point in the southeast of Iran, was a low-pressure, high-suction system that created dust storms in the region.

The results of dust storms monitoring in northwest and west of Iran using satellite images showed that remote sensing techniques due to extensive and continuous coverage in space, monitoring natural disasters and also monitoring dust storms, intensity and dynamic tracking, can play a major role in dust monitoring. The results also showed that the Aqua and Terra satellites have a high capability in detecting and tracking dust storms due to their high temporal, spatial, and spectral resolution.

Sustainable management of soil and land resources requires the identification of factors affecting their development or degradation. Accurate and reliable determination of the distribution of soil and landscape properties is the basis of such identification. In this regard, it is necessary to prepare continuous location maps. In soil surveying, soils are generally collected by a point-by-point sampling method and soil properties between these points are estimated by interpolation methods. Soil salinity is one of the most common challenges in arid and semi-arid regions of Iran, which leads to land degradation by declining soil quality. Therefore, monitoring soil salinity is needed to overcome the aforementioned problem. The accuracy and precision of Kriging, as one of the major geostatistical methods, depend on the size, distribution as well as density of soil samples. Due to the use of these maps in soil planning and management for the future, their accuracy and precision are of great importance. This study aims to evaluate the role of grid sampling patterns on the quality and efficiency of final soil salinity maps.

The study was conducted in Shamlou region with an area of about 155 ha. It is located in Heris County, East Azerbaijan Province comprising abandoned cultivated lands. The dominant soils across the study area were Inceptisols and Aridisols. Based on the main objective of this research, five sampling patterns were designed: I) uniform grids of 100 m; II) uniform grids of 200 m; III) offset grids of 200 m; IV) rectangular grids (100×200 m) with vertical direction; V) rectangular grids (100×200 m) with the horizontal direction. A total of 155 disturbed samples (0-20 cm) were taken in the study area. All the collected samples were transferred to the laboratory for analysis. After providing the soil extracts, ECe was measured. The Kriging method was also employed to predict the spatial distribution of soil salinity according to the above-mentioned patterns. The accuracy of prepared maps in a classified mode was also evaluated. Finally, the efficiency of each map was evaluated using the Average Size Delineation (ASD), Index of Maximum Reduction (IMR), and Delineation Density (DD) criteria.

The maximum ECe in the study area was reported to be 36.5 dS.m-1. The provided maps based on the use of various sampling patterns showed that the salinity of the west part of the area was higher than the east one. Geostatistical analysis revealed that the spherical model can be identified as the best-fitted model for a 100×200 m rectangular grid with vertical direction, while the exponential model was the best one for the rest patterns. The results demonstrated that the least and the highest values of nugget and range were observed for 100 and 200 m uniform grids, respectively. Since the index of nugget/sill illustrates the spatial dependence of soil salinity, it was found that management has no role in the spatial distribution of salinity using all studied patterns except 100×200 m rectangular grid with the vertical direction. The t-test results indicated that there is no significant difference between the predicted and actual values. According to the R2 values, the best sampling pattern was found to be uniform grids of 100 m, followed by, rectangular grids with horizontal direction, offset grids, rectangular grids with vertical direction and uniform grids of 200 m. The next step was to assess the maps (with a scale of 1:13337) efficiency indices. It was found that the maximum location accuracy, minimum legible delineation (MLD), optimum legible delineation (OLD) and optimum legible area (OLA) were 1.33 m, 7115 m2, 1.6 cm2 and 2.84 ha, respectively. The lowest average size delineation (ASD) was found for uniform grids of 100 m while the highest one was for rectangular grids with vertical directions patterns. A similar trend was also observed in terms of index maximum of reduction (IMR). Furthermore, the optimum delineation density (DD) was found to be 4.59 for the offset grids of 200 m pattern.

The results showed that sampling point distribution had a more important role than sampling point density in selection of the optimum model for interpolation. In terms of nugget and range, this role was demonstrated in an inverse manner. Since the nugget/sill index (taken by all studied sampling patterns in the same results) revealed that salinity has a strong spatial distribution, the density and distribution of sampling points did not play an important role. Although the distribution of sampling points had a role in the accuracy of interpolation, the sampling point density was more effective. The results showed that preparation of high resolution maps with many details does not always require a large density of sampling, but in patterns with the equal densities, the efficiency of maps depends on the distribution of sampling points. Also, there was no direct relation between optimum delineation density and specific density as well as distribution of sampling points. Therefore, prior to the selection of suitable distribution for soil sampling patterns, it is recommended to find the optimum sampling density for the project.

Nowadays, demand for water is increasing especially in arid and semi-arid regions (e.g., Iran) due to population growth, economic development, higher standard of living, and changes in consumption patterns. Hence, optimal management of water resources in these areas is essential. Furthermore, climate change and increasingly extreme weather events have caused a surge in natural disasters (e.g., floods) over the past 50 years in arid and semi-arid regions. Thus, forecasting and modeling of runoff data is extremely necessary for planning and managing of water resources. Water flow forecasting plays a key role in flood reduction, reservoir optimization, and reservoir management. These models are mostly developed and applied for simulation and prediction. Therefore, different types of forecasting methods have been proposed over the decades including: Box and Jenkins (SARIMA), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Genetic Algorithm (GA) models. Forecasting hydrological reactions invariably involves uncertainty. So far, numerous studies have been performed to improve the reliability and accuracy of hydrological forecasts, resulting in reduced risk error. Therefore, the main objective of current research was to use artificial intelligence methods consisting of ANN, ANFIS, GA, and SARIMA models to predict monthly runoff data and also select the best model for the efficient management of water resources in the Gharasou River basin.

Gharasou river basin with an area of 1624 square kilometers is located in the west of Golestan province and has an important role in providing water resources required in this province. In this research, to model and forecast the runoff process, the monthly runoff time series of 4 hydrometric stations of Pol-Tuskestan, Naharkhoran, Ghazmahale, and Siah-ab over Gharasou River basin were used for a period of 36 years (1982-2018). The time series homogeneity was examined using the Chow`s method. Runoff data are time dependent, so initially these data were arranged in time series. After sorting the data, four models consisting of Box and Jenkins (SARIMA), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Genetic Algorithm (GA) models were used to forecast monthly runoff. To increase the prediction accuracy of other methods, the far time series of monthly runoff were first ignored based on to neural network method and then the number of effective years for modeling was determined. Later, the monthly runoff was forecasted for the next 12 months using four models consisting of SARIMA, ANN, ANFIS, and GA. Lastly, based on the forecasted values and using MAD, RMSE and MAPE indices, the accuracy and precision of SARIMA, ANN, ANFIS and GA models were compared. Modeling and forecasting were done using Minitab, R and SPSS software packages.

Based on the type of distribution of monthly runoff and the presence of zero data, log(1+Yt) conversion was used in the models to stabilize the variance. The results according to the autocorrelation diagrams revealed that the time series in all stations have seasonal trend with a period of 12 months. Then, the monthly runoff of the next 12 months was forecasted using four models including SARIMA, ANN, ANFIS and GA. Model validation results using three indicators of MAD, RMSE and MAPE revealed that the ANN model in the three hydrometric stations of Naharkhoran, Pol-Tuskestan and Siah-ab had the best performance. In these three hydrometric stations, after ANN model, the ANFIS model has been selected as the most suitable model. However, the performance of these two models has been very similar. In the Ghazmahaleh hydrometric station, two models of ANFIS and ANN had the best performance, respectively. In this study, it was also found that in four selected hydrometric stations, the GA model had a good performance after the two models of ANN and ANFIS. Although SARIMA model performed very well in identifying the trend of monthly runoff changes, it had the weakest performance among the methods. The forecast data using SARIMA model were overestimated compared to the actual data for March and April, but in other months, the forecast data using this model were relatively appropriate.

In this research, to model and predict the monthly runoff process, four models including SARIMA, ANN, ANFIS, and GA models were used for four selected hydrometric stations in Gharasou River basin. The results of model validation using three indicators of MAD, RMSE and MAPE showed that the ANN and ANFIS models had the best performance among the four models used. It was also found that time series of runoff data in hydrometric stations have undergone structural changes due to the physical and climatic alterations in the upstream of rivers. Therefore, the distant past of time series may cause deviations in modeling and forecasting results. To overcome this problem, making use of an algorithm to select the number of effective years in modeling and forecasting can be useful. Artificial neural network provides a suitable criterion for selecting the number of effective years due to its high accuracy in modeling and forecasting. Based on the effective years identified by this model, other models can be modified and provide more appropriate input data for forecasting from other models.

Rivers are considered as the main sources of water for humans and other organisms, and sometimes this source of life causes destruction and irreparable damage. Predicting the hydraulic behavior of rivers in the face of potential floods to reduce damage to urban and rural areas, facilities under construction, farms and other existing uses around the river is of particular importance (Askari et al., 2014) because they can be used to provide measures and solutions to control floods and minimize the damage caused by it. One of the key topics in geomorphology, engineering and river management is the morphology of river canals, which provides useful information about the geometric shape, bed shape, longitudinal profile, cross sections and their deformation and location over time (Yamani et al, 2012). ). Hejazi et al. (2020) zoned for flood risk in the Varkeshchay catchment using the HEC-RAS model. The results of these researchers showed that 110 km of the total catchment area is affected by floods with a return period of 50 years and 63 km of it is affected by floods with a return period of 25 years. Shafiei Motlagh and Ebadati (2020) used HEC-RAS software (Case study: Maroon River - southwest of Iran) to perform zoning of the flood and simulating the hydraulic behavior of the river. They concluded that the flood area for different return periods of 5, 10, 25 and 50 years is equal to 1265, 1651, 2334 and 4450 hectares, respectively, and the number of endangered villages is equal to 5, 3, 2 and 9, respectively. In a study aimed at producing flood risk maps in Kazakhstan, Ongdas et al. (2020) stated that the village of Volgo was flooded during a 100-year flood event. Aynalem (2020), in a study on the Muga River, reported floodplains for 5, 10, 25, 50, and 100-year return periods of 18, 21, 26, 34, and 43 km2, respectively. Therefore, the aim of the present study is to morphologically simulate the occurrence of floods in the Nooranchay River using the HEC-RAS hydraulic model.

Geological maps 1: 100000, topographic maps 1: 50000, and 1: 2000, data of synoptic stations, rain gauges, and flowmeters are among the most basic data of the present study which were prepared by Ardabil Regional Water Organization. HEC-RAS software or software river US Army Engineering is a set of tools that allows the user to perform river hydraulic calculations in steady-state and non-steady-state flow. The HEC-RAS system includes three components of one-dimensional hydraulic analysis to perform water level profile calculations in steady-state flow, non-steady-state simulation and sediment transport calculations at the moving boundary. These three components share a common geometric representation and use the same geometric and hydraulic calculation process is set of tools that can be used in the GIS software environment. This add-on creates a link between ArcGIS software and HEC-RAS software, and is specifically designed for spatial data processing for use in RAS modeling and for processing RAS results in the GIS environment. Processing ground data and other GIS data in ArcGIS software using GEO-RAS allows the user to create and export a geometric file for RAS analysis. To perform hydraulic calculations using the HEC-RAS model, first the cross sections must be defined, for which the desired layer of the TIN map is extracted in the ArcMap software environment. After forming the TIN layer, different layers such as center flow line layer, river bank line layer, flow range layer and cross-section layer are drawn and after processing by ArcMap software, it is ready to be extracted for HEC-RAS hydraulic model work. The HEC-RAS model can perform water level profile calculations for Gradual variable steady flow in rivers and artificial canals in subcritical, supercritical and mixed flow regimes.

The TIN layer is the basis for extracting the alignment lines and the required RAS layer, and the more accurate the obtained river elevation figure, the closer the 3D model will be to reality. In this study, due to the use of topographic map 1: 2000 and also the adaptation and casting of existing maps on the ETM satellite image of the region, it was found that TIN obtained from digital maps is able to significantly simulate floodplains and plains around Nooranchai River. The basis can be a good reference for conducting research and creating flood simulation layers of the Nooranchai River. The minimum altitude is 1220 meters and the maximum altitude is 1530 meters above sea level. According to the flood zoning map (Figure 11), the flood area with a return period of 2 years along the Nooranchai River is about 122 hectares. These zones mainly correspond to the bed of Nooranchai river, which locally surround the river channel. The average width of areas exposed to floods with a return period of 2 years is about 160. In general, such floods do not pose a threat to human communities living in urban and rural areas. Finally, the highest flood zone with a two-year return period covers the lower part of the Nooranchai River, which has entered the Ardabil plain, and the lowest flood zone with a two-year return period can be seen upstream of the Nooranchay River. However, these floods are of great importance in the formation and morphological changes of the Nooranchai River duct due to their high frequency and potential for shaping the channel platform. Also, the average flood width of 50-year-old floods is about 307 meters. These floods cover flood zones with return periods of 2, 5, 10 and 25 years. As a result of this increase in area and width in areas (3), (4), the areas leading to the Ardabil plain, which are lower than areas (1), (2), has led to more flood zones in the above areas This has caused the flooding of agricultural lands around the Nooranchai River, and even damaged some residential areas of Ardabil and the villages through which the Nooranchai River passes, and even resulted in casualties. In general, such floods can pose a threat to human communities living in urban and rural areas. Finally, the highest flood zone with a return period of 50 years is downstream of Nooranchai River, which flows through Ardabil, and the lowest flood zone with a return period of 50 years can be seen upstream of Nooranchay River. The impact of floods with a return period of 200 years along the Nooranchay River increases by about 329 hectares. Also, the average flood width of 200-year-old floods is about 500 meters. These floods cover flood zones with a return period of 2, 5, 10, 25, 50, 100 years. As a result of this increase in area and width, more can be seen in all parts of the upper, middle and lower reaches of the Nooranchai River. In other words, during the return period of 200 years, the flood zone of Nooranchai River has covered all parts of the river. Due to high discharge and the participation of discharges of different tributaries, such floods can affect a large part of the floodplain area of ​​the river and in addition to human and financial losses and destruction of agricultural lands, have many morphological consequences such as shortcuts, and so on. Floods with a return period of more than 200 years affect the residential areas of the villages around the Nooranchai River and even the riverbed in the part entering the Ardabil plain; they can also affect part of the residential areas of Ardabil.

Considering the flood simulation of Nooranchai river using HEC-RAS hydraulic model, it was concluded that it shows a very high spatial variability of flood risk along Nooranchai river. This variability stems from variable geomorphological conditions along the river. The results show that floods with a return period of 2 years do not pose a serious threat to human communities living in the vicinity of the Nooranchai River. These floods mainly affect the agricultural lands along the river. The risk of floods also increases with increasing return periods. Floods with a return period of 50 years due to the inclusion of a large area of ​​the river and affecting residential areas will lead to property and human losses. These floods can also lead to morphological changes around the river, as well as extensive damage and loss of life and property. Rather similar findings were obtained by scholars such as Rad et al. (2018), who conducted a study in Khorramabad watershed located in Lorestan province and indices such as flow boundary conditions, maximum instantaneous flow with different return periods, cross sections and their distance and manning roughness coefficient for each section in HEC-hydraulic model RAS implementation and water level profile were obtained in different flood return periods. In addition, Shafiemotlagh & Ebadati (2021) used HEC-RAS software to zone the flood and simulate the hydraulic behavior of the Maroon River. They concluded that the flooding area for the return periods of 5, 10, 25 and 50 years is equal to 1265, 1651, 2334, 4450 hectares, respectively, and the number of endangered villages is equal to 5, 3, 2 and 9, respectively.

One of the most important natural processes in arid regions is semi-arid wind erosion. Sand dunes are one of the most important facies of wind erosion. Sand dunes in the field of wind process are among the most dynamic geomorphic features of the earth's surface, which, on the one hand, are affected by the characteristics of speed, direction, wind frequency and, on the other hand, are affected by the characteristics of the earth's surface and sedimentary materials. Probability-based wind prediction for sand transport phenomena is a key element for human activities in arid regions. Wind erosion and movement of quick sands, which is considered as one of the important processes of land degradation and a serious challenge in Iran, occurs due to the interaction between climatic and terrestrial processes. The phenomenon of dust and the movement of quick sands is considered one of the important processes of land degradation and a serious challenge in Iran, and on the other hand, coastal areas are sensitive lands that have been affected by both sea and land ecology. They are also unique in terms of ecosystem diversity. Therefore, knowing the status of wind erosion and the activity of wind sediments, especially in landforms sensitive to this destructive environmental phenomenon, as well as identifying climatic factors affecting it can be an effective step in reducing desertification and improving air quality, especially in arid and semi-arid areas.

According to the purpose of the study, data on wind (frequency and direction), rainfall, temperature and number of dust days related to the synoptic station located in Jask city during a 20-year statistical period (2001-2021) were obtained from the Meteorological Organization. WR-Plot View 7 software was used to analyze the anemometer data. Also, using the mentioned capabilities of the software and considering the basic speed equal to the threshold speed of wind erosion, annual and seasonal storm rose were prepared. Land use maps and sand maps of the study area were prepared using aerial photographs, Google Earth satellite images and field visits.

Statistical analysis of climatic data in Jask region during the period (2001-2021) showed that significant annual changes in annual rainfall, temperature and wind occurred, so that the annual rainfall trend decreased significantly. The trend of annual temperature changes has not been significant, but the temporal changes of wind have taken a relatively downward trend and have shown a significant decrease in recent years. According to the annual windfall of Jask station, it was observed that 7.7% of the total annual observation hours, the air is calm and without direction, 3.92% of the winds blow in different directions and the speed is more than 0.5 m/s. The prevailing wind direction is from the west in all seasons and from the east in summer. The results of classifying the percentage of wind speed frequency showed that about 80% of winds have a speed of less than 6 m/s. The trend of changes in erosive winds during the statistical period under study has an upward trend until 2014 and in recent years has a decreasing trend so that in 2021 the lowest frequency of erosive winds was observed. The results of the portability calculation showed that the annual average of DP is 257 units. The final direction of movement of the sand is at an angle of 280 degrees to the north and the UDI index (RDP / DP) is 0.3, which indicates the variability of low wind directions. The Golmaseh diagram also showed that the wind sediments were moving to the northwest. The highest sand carrying capacity was seen in 2014 and the lowest in 2021. Considering the average of 257 vector units for Jask, the capacity of the amount of sand that can be moved annually is estimated to be about 18 cubic meters per unit width. In terms of land use, most of the area covers the rangeland. Also, sand zones cover about 5.7% of the area, which is mainly located in the south and southeast. The results of the interpretation of aerial photographs, satellite images, as well as field visits to the sand dunes of the southern regions of Iran, indicate the existence of sand dunes of the type of transverse hills, Barkhan and Seif, which are the result of two-way winds from one sector is blowing.

80% of winds have a speed of less than 6m/s, 20% of winds have a speed of more than 6 m/s. The monthly distribution of wind speeds below the threshold indicates that the highest number of erosive winds was observed in summer and the lowest frequency was observed in autumn. The wind direction is dominant throughout the year and all seasons except summer from the west. The amount of sand carrying capacity based on the threshold speed of 6.5 m/s is about 257 vector units moving from the southeast (coastal lands) to the northwest (coastal lands), which indicates that the direction of transfer of most sands from relatively arid and saline coastal areas (coastal grasses) are towards the plains and mountainous lands along the southern coasts of Iran. The ratio of vector to annual algebraic output (UDI) of this station is 0.3, which indicates composite multi-directional winds with sharp angles of variability of wind directions at low levels. Existence of sand dunes of longitudinal hills, seifs and silks is the result of two-way winds blowing from one segment. In the southern coasts of Iran, most of the main winds from the southwest and sub-southeast winds from the coast to the sea are effective in the formation of sand dunes. The strongest winds of the study area blow in summer and the weakest in autumn. The trend of changes in sand transport potential also showed the trend of wind energy in Jask station, which for most years has the ability to carry wind sediments in the middle class (DP> 200> 400), but a significant decrease was seen in 2018 to 2021; the highest sand carrying capacity was seen in 2014 and lowest in 2021. Also in the study station, the RDP/DP ratio has a range of changes between 0.022 to 0.8 in almost all seasons and throughout the year. The area of sandy regions has increased from 72.260 to 82.925 km2 between 2013 and 2021, which shows an increase of 10.66 m2.

Spatial changes in soil properties are significantly influenced by factors affecting soil formation such as topography. Topography can create different properties in the soil by affecting the spatial distribution of effective environmental parameters. Topographic features such as contour, slope direction by affecting soil temperature, evaporation capacity, soil moisture, soil organic matter, precipitation, movement and accumulation of soil solution can affect the biological properties of soil. Respiration of soil microorganisms is one of the most sensitive and important biological properties that can affect soil quality. To study the impact of land use change on soil ecosystem performance due to human activities, it is necessary to maintain and restore soil capacity to provide ecosystem services. The aim of this study was to investigate the effect of slope and height classes on soil microbial respiration in agricultural, rangeland and forest areas of Khanghah Namin village.

In order to conduct this research, in the fall, samples of intact and untouched soil from the study area were prepared in 72 points by regular networking method with distances of about 200 meters. Common physical and chemical properties of soil including soil pH, electrical conductivity, equivalent total calcium carbonate, organic matter, soil texture (percentage of sand, silt and clay particles) and total porosity were measured. From soil biomarkers, basal microbial respiration and substrate-stimulated respiration were determined. Kriging geo statistical method was used to estimate and determine the spatial distribution and evaluate some soil properties as well as cumulative soil quality indicators. For this purpose, the geographical coordinates of each point of the GPS device were transferred to GIS software. After recording the data of the sampled points, the interpolation command was executed and the information of the non-sampled points was obtained. In order to explain the spatial similarity of the variables, the experimental variogram of the data was obtained using GS + statistical software and the best variogram model was selected according to the variogram information. Finally, the distribution map of soil biological indicators was extracted using GIS and Kriging geo statistical methods. The correlation between the measured and estimated values of soil biological properties was calculated and evaluated in SPSS19 statistical software. Experimental factors included three land uses (namely, agriculture, rangeland and forest), slope direction at two levels (north and south) and two elevations (<1580 <) in terms of significant mean difference in field moisture. Normalization of data was performed by Shapiro-Wilk (1965) test using SPSS19 software. Mean comparison was performed unpaired by T-Test using SPSS19 statistical software. Pearson correlation between soil microbial base respiration and substrate-stimulated respiration with other soil properties was obtained in SPSS19 software.

The results of the descriptive statistics of the soil properties showed that most of the soils in the study area have a loam soil texture class. The pH value of the samples was in the range of 5.7.7, the percentage of organic carbon was 0.6.6 % and calcium carbonate value was from 0.67 % to 16.67 %. The maximum electrical conductivity (EC) was less than 2 ds/m of Siemens per meter, indicating the non-saline soils of the area. Also, the high levels of average microbial base respiratory and the substrate induced respiration were 1.21 and 7.5, respectively. High correlation between the properties of soil organic carbon percentage, porosity percentage, silt percentage, farm moisture percentage and soil pH with base microbial respiration and substrate induced respiration with clay, sand and electrical conductivity of the soil were not significant. The farm moisture had the highest hydraulic soil with the lowest correlation with microbial respiration. In general, the average base and substrate induced respiration was higher on the height contour (1580<). Also, both base and substrate induced respiration in accordance with elevation had a significant mean difference at 5 %. In the present study, the studied slope on the north and southern slopes on the base microbial respiration level at 5 % probability level and substrate induced respiration with the probability level of 1 % was significantly different. The highest amount of these indicators was measured on the northern slope of the area and the lowest in the south slope of the southern south slope located mostly in the center to the northeast of the area and the northern slopes in the southern and northern parts of the area. It is maintained that the cause can be attributed to the existence of heights and solar radiation on the southern slopes. To determine the distribution of soil biological indexes at the level of the study area, the distribution of the area's biological properties of the area was prepared by applying the Kriging method. The best model for drawing soil base respiration map was selected for substrate induced respiration with the largest soil bed based on the highest R2, the range of impact, the least RSS and the effect of the piece. The largest amount of soil respiration was observed by the base respiration and irritated respiration in the north and northeast of the area. It is also observed that as the height of the area increases from west to east, basic respiration is generally stimulated with a relatively increased trend. The results show that there is a significant correction in both characteristics of measured and estimated values ​​by the Kriging internalization method.

In this study, the amount of basal and bed-stimulated respiration in forest lands was significantly higher than rangeland and agricultural lands. Between rangeland and agricultural uses, the amount of soil microbial respiration in agricultural lands was lower. Due to the fact that this area is a tourist destination and there are local livestock, this finding can be attributed to transportation, high human and livestock activity at lower altitudes, and agricultural and pasture use, which are more exposed to these activities. The microbial respiration values ​​in the northern direction of the slope were significantly higher than in the southern direction. The southern slopes received more solar energy than the northern slopes. Usually in the south-facing slopes, the number and activity of soil microbial community is low due to high temperature, low water, low porosity and adverse consequences due to inadequate vegetation. In addition, a significant mean difference was observed in the northern and southern directions of the slope. Also, the increase in altitude due to the relative increase in humidity and decrease in temperature led to an increase in microbial respiration. This issue can significantly affect the activity and respiration of soil microorganisms by affecting soil processes and its development as well as the amount of vegetation in the two slopes. According to the obtained results, it can be stated that the evaluation of biological properties that was discussed can play a prominent role in controlling the management factors in the area. Large changes in soil respiration indices are probably due to the high sensitivity of these properties to human factors such as tillage operations and the use of chemical fertilizers, which cause changes in soil surface uniformity. The zoning map of these two features also confirmed this. The results showed a logical correlation between soil microbial respiration and other characteristics that the existence of a correlation between soil properties indicates a strong relationship between them. Geo-statistical science in this study was able to estimate the properties well. The results of this study showed that in the studied area, biological indicators depend on the position of the landscape and land use, and these factors can affect the structure, development and evolution of soil by affecting the structure of the microbial community.