نشریه مهندسی اکوسیستم بیابان
پیاپی 24 (پاییز 1398)

Many natural phenomena have many variables that make it difficult to find relationships between them using common mathematical methods. This problem, along with the impossibility of measuring all elements of nature, has led to a major evolution in the way of understanding and explaining phenomena. In this way, one can use the fractal geometry with the theory that many natural phenomena are order in the chaos. Each element of nature is represented as a fractal geometry number. In fact, fractal geometry is a quantitative tool for studying the geomorphology of drainage networks and modeling many complex natural phenomena. Since geological variables have a profound effect on the nature and activity of the drainage networks. In this study, the role of lithology and geological formations is studied to quantify the drainage networks and used fractal dimension to indicate sensitivity to erosion of the formations of this area.

The present study consists of four main sections. The first section is the collection of maps and data. In this section, geological maps of 1:100000 areas were provided and selected from the geological formations of Yazd- Ardakan basin, three geological formations of Kahar, Granit and Taft. Sensitivity to erosion of these formations was studied in this area using PSIAC, Feyznia and Selby methods. In second section, fractal dimension is estimated in 30 plots of 1 × 1 km2, three geological formations of Kahar, Granite and Taft. In each geological formation, fractal dimension was calculated by box counting method using Fractalyse software and the number of plots required in each formation was determined using Graphical method. In the third section of this study, morphometric indices were calculated including drainage density, number of ranks, average length of rank and rank frequency. In the final section, uncertainty analysis of fractal dimension efficiency was studied in classification and separation of geological formations. In most studies in the field of fractal dimension, the uncertainty of this method is not considered. As a result, their findings do not have enough accuracy to generalize to natural phenomena. Therefore, in this study, uncertainty of fractal dimension efficiency was investigated in 30 plots of 1 × 1 km2 that were excluded from other study sections for testing this method.

The mean fractal dimension of 1.149 represents Taft formation, the mean fractal dimension of 1.161, is Granite formation and the amount of 1.207 is Kahar formation. The highest fractal dimension was calculated in Kahar formation (1.279) and the lowest in Taft formation (1.046). In addition, the correlation coefficient is 99%. The results also indicated a significant and meaningful relationship between fractal dimensions of drainage networks and morphometric properties. In this paper, a positive relationship is observed between morphometric parameters and fractal dimension, so that the greatest correlation coefficient is found between the fractal dimension and the drainage density (0.99). The results of uncertainty analysis of fractal dimension showed which fractal dimension in the geological formation of Kahar with 90% has a steady trend and in two geological formations of Taft and Granite, this amount is about 70%. Therefore, fractal dimension in the drainage network identification of the Kahar formation is more powerful than two other granite and Taft formations.

The drainage networks are the most prominent landscapes on earth that are the basis of many hydrological and geomorphological models. Due to the geomorphological characteristics of the region, the drainage network shows its own fractal properties that are saved as code in it. In fact, drainage networks are fractal phenomena with fractal behavior. In this study, the fractal dimension and morphometric properties of the drainage network were used to analyze the sensitivity to erosion of the geological formations of this area. PSIAC and Feyznia methods did not perform well in separating the sensitivity to erosion of Taft and granite formation, and they are unable to distinguish between these two formations. So, using the Selby method, six effective factors were investigated on the resistance and sensitivity of formations to erosion (Schmidt Rebound Hardness, weathering, distance between joints, direction of the joints relative to the slope, the width and connection of the joints) in Taft and Granite formations. The study of these factors leads to field visits and spends a lot of time and cost. As the results of the resistance to erosion of formations were shown by Selby method, Granite formation has less resistance to Taft formation. Due to the climatic conditions in this area, Granit formation is susceptible to weathering. Extreme weathering acts as arenization process. In fact, fractal dimension in three geological formations of the study area is well showed the difference in resistance to erosion of both Taft and Granite formations. The results of this study showed that fractal dimension allows for a quick and accurate analysis of the erosion characteristics and sensitivity to erosion of the formations of this area.

The main feature of Iran's rainfall is their variability; in fact, changes in rainfall are due to changes in their producing factors. The study of rainfall changes in the country showed that the spatial variations of precipitation from west to east and north to south decreased and these changes are well-coordinated with Iran's major rough nesses. The highest rainfall of the southern shores of the Caspian Sea is controlled by migratory cyclone, but about half of the annual precipitation and the most severe precipitation occur in the area of the Siberian highlands in the north of the Caspian Sea. The purpose of this study is to analyze the nature and structure of Variation in atmospheric spring rainfall in Iran. Unlike in-country studies that have been conducted more regionally, this study attempts to study the entire region of Iran.

According to the purpose of the study, two databases have been used: Environmental data: This group of data was obtained by interpolating the daily precipitation values for the statistical period from 1961 to 2010. . For this purpose, the daily precipitation data from 573 synoptic, climatological and rain gage in a 50-year period (1961-2010) is extracted. After extraction rainy days inclusive, corresponding pressure data, was extracted using the programming capabilities in the GRADS software environment and have been identified by the cluster analysis of representative days and Iranian spring weather patterns in the MATLAB software environment. After the creation of the database for the determination of the rainy day, three criteria were considered; Daily precipitation of 1 mm and above, minimum continuity of two days and at least 50% coverage (without spatial continuity).
Atmospheric data: For this study, variables such as sea level pressure, geopotential heights, Special moisture   Wind Meridian and orbit, Atmospheric moisture flux, Perceptible water and vorticity were studied. After extracting rainy days inclusive, Cluster analysis has been used to identify the rainfall patterns. Lund's correlation method was used to select the representative day. Therefore, to select the representative day, the day that has the highest similarity with the maximum number of days in the group has been selected. The correlation coefficient represents the degree of assimilation of the two map patterns with each other so, the day with a greater number of days, the coefficient of correlation of 0.55 was selected as the representative day. After extracting the representative day, in order to analyze the dynamical equilibrium of them, variables such as sea level pressure, geopotential heights, and Special moisture   Wind Meridian and orbit, Atmospheric moisture flux, Precipitable water and vorticity were analyzed.
 

The results showed that the highest amount of spring rainfall in Iran was due to the intense pressure changes the Caspian Sea East Turkey-low pressure, low pressure Saudi Arabia. While the most prevalent spring weather pattern of Iran, the Siberian-low-pressure Siberian strain of Saudi Arabia has been the low pressure of Sudan. So that by creating a strong pressure gradient on Iran and deployment Mediterranean landing deep in the atmosphere has led to instability in Iran. The status of moisture sources also indicates that the role of the Arabian Sea, the Persian Gulf, the Red Sea at 850 and 700 hPa, is clearly visible and detected in the spring humidity flux of Iran. In addition to providing the humidity of the coastal areas at the sea level, the Caspian Sea is one of the main sources of spring rainfall in the northwest and northeastern regions at 700 hPa; while the Arabian Sea plays the most role in the humidity flux towards the southern and western parts of Iran. Check the status of atmospheric vorticity showed the highest levels of instability in the atmosphere above the atmosphere, because of Westerly that has been accompanied by different pressure systems. In this regard, the maximum degree of divergence of the atmosphere occurred when the Siberian High Pressure was active and its tabs on Iran.

Spatial Distribution of Dynamic Matching Patterns showed that Iran's spring rainfall was influenced by five cycling patterns. The results showed that spring Inclusive occurred when severe compressive influences were caused by the influence of the high-pressure cold-water systems in the North and the warm and humid masses of the southern regions, this leads to instability and vertical ascent is air.  However, divergence and negative vorticity maximum amount of time by which the Siberian high-pressure tanks have been located in Iran, and the highest concentration of convergence and ascent and unstable air occurred during the deployment of the Saudi and Persian Gulf and the intensification and intensification of it at the high altitude of the atmosphere.

Iran's plateau, especially in different regions of Iran, is composed of different climates, while in its southern areas summer temperatures reach 50 degrees above zero, the Caspian Mediterranean region is running wet weather. One of the most important indicators of climate difference is the issue of water. Today, water supply is one of the most important concerns to realize the goals of sustainable development and challenges are in most countries in the world (Zabihi et al., 2015). For this reason, the identification of areas with groundwater potential is one of the important tools for conservation, management and exploitation of groundwater resources (Zabihi et al., 2016). In an exploratory study, Mehran groundwater potential assessment was conducted with the entropy model and random forest log algorithm (Rahmati, et al. 2016).

Jahrom is one of the cities of Fars, located in the southern part of this province with an area of 5,768 km2. The maximum and minimum altitudes of the study area are 3166 and 766, respectively. These altitudes are part of the Zagros folding, and for most of the mountains, they are located in the southern part of the Southern Zagros Mountains, and its branches are surrounded by the city. In the present study, 13 variables including geological layers, slope, tilt direction, elevation, distance from fault, fault density, distance from the waterway, water congestion, land use, topography humidity index Surface curvature, curvature of the waterways, slope length using the experiences of experts and researchers in the surveys have been used in the study area. At first, a digital elevation model with a spatial resolution of 10 meters from the mapping organization of the country was prepared and the slope, height and gradient layers were directly extracted from the ArcGIS software. The map of distance and density of the region fault was prepared using ArcGIS software using 1: 100000 Geological maps of Iran Geological Survey and Mining Exploration. Water dependent factors such as TWI were calculated directly from the digital elevation model in software (SAGA-GIS, ArcGIS) according to Equation 1 (Moore et al., 1991; Jafari et al., 2014). In the entropy model, variables with the maximum effect on the occurrence of an event are determined. (Bodarik et al., 2010; Constantin et al., 2011; Pourghasemi et al., 2012).

In order to evaluate the produced models, the relative recognition function curve (ROC) has been used (Pourghasemi et al., 2012; Porruthi et al., 2014). The ROC curve is a graphical representation of the equilibrium between the negative and positive error rates for any possible cutoffs (Pourghasemi et al., 2013). The relative performance index is a curve whose vertical and horizontal components are calculated from the comparison matrix with the definition of the threshold between zero and one. Shannon's entropy model shows that the AUC was 83.9%, which is better than classical forestry algorithm model, whose AUC is equal to 76.20%, and this level is very good.

Considering the experimental springs located in the potential springs of the spring, it reveals the fact that with the increase in the potential spring levels of the spring, there has been a significant increase in the density of springs in these areas, while experimental springs have not played a role in the modeling. Many of the pilot springs are located in a medium and high potential area, and springs located in an inappropriate and low potential class are insignificant (Zandi et al., 1394). Validation of the maps obtained by assessing the ROC for the zoning of groundwater potential susceptibility confirms that Shannon entropy model with a 0.839 percent estimation compared to the random forest log algorithm with an estimated 762 percent accuracy has a better accuracy.

Climate change and human activities have a direct impact on land vegetation. Decreased rainfall and increased temperature are among the climate change factors leading to significant changes in water resources and energy balance in affected areas. On the other hand, human activities such as growing population, overgrazing and land use changes that make change in land conditions, also cause land degradation and desertification. Land degradation is a negative environmental process defined by many authors using different methods. Some authors have defined land degradation as reduced potential for land production or long-term decrease in ecosystem performance, while others have determined land degradation as decreased resources over time due to negative effects of human activities. Despite these definitions, all definitions imply that land degradation causes decrease in potential of land resources to meet the ecosystem needs. There are several methods for assessing land degradation at different spatio-temporal scales. Land degradation is the result of a prolonged decrease in vegetation and initial production at spatio-temporal scales. Therefore, long-term study of vegetation can be used as a strong index for assessing land degradation. According to studies, one can conclude that arid and semi-arid regions are very sensitive to climate change, and on the other hand, vegetation well illustrates these changes. Therefore, due to the fact that Fars province occurs in arid and semi-arid regions of Iran, this study aims to investigate the process of land degradation using trend analysis of climate data and vegetation indices in Fars province.

Fars province covers 122000 km2, which accounts for 7.4% of the country's total area. Monthly products of NDVI index from Terra satellite, MODIS sensor MOD13A2 were used in this study in order to investigate vegetation. Also, climate data of temperature and precipitation with the monthly lag from the synoptic stations in the region, with appropriate distribution at the province scale and a common time base during the period of 2006-2016 were used. The NDVI products were used to calculate vegetation index for the period of 2006-2016. Google Earth software was used to assess NDVI values in different regions of Fars province. In order to collect the field data, the available images from Google Earth for each year were taken as random sampling points, and then the consistency rate of vegetation and NDVI index were evaluated. Also, we use used MODIS land cover-type product (MCD12Q1, 500 m) for land cover information 2006–2016. After obtaining climatic (temperature and rainfall) data for the stations in the present study, temperature and rainfall maps were prepared using inverse weighted distance interpolation method. Inverse Weighted Distance (IDW) method interpolates the unknown quantity by weighing the data around the point. Mann-Kendall test and Theil–Sen estimator were applied to calculate the monthly changing trends of vegetation, temperature and rainfall in the study area. Theil–Sen estimator was used to confirm the accuracy of the trend changes. After analyzing the trend of climatic data and vegetation index, the correlations between these indices were investigated. The linear correlation model was used for monthly NDVI, temperature and rainfall time series. All statistical analyses were performed in IDRISI software on a monthly basis for the period of 2006-2016.

The changing trend of vegetation, rainfall and temperature showed that these indices show different changes during the period of 2006-2016. Changes in NDVI index indicate that the value of this index varies from 0 to 0/55. The changing trend of NDVI index showed a decreasing trend for this index during the time period, but climatic indicators of temperature and rainfall showed increasing trends. The trend of changes in NDVI index showed that this trend is not the same throughout Fars province so that 22.4% of the area of Fars province showed a decreasing trend in terms of vegetation. On the other hand, the decreasing rend of vegetation was significant for 7.8% of the study area. 13.2% of the area of province indicated no correlation as well as no trend for this period. The increasing trend for vegetation accounted for 64.4% of the area of province. The analysis of rainfall trends showed that this index had a decreasing trend in 86.1% of the area of the province. Rainfall changes without any trend accounted for 9.7% of the province's area, while the increasing trend for rainfall was found for only 4.2% of the province. The study of the temperature trend showed that only 2.4% of the province showed a decreasing trend. About 0.02% of the province showed temperature variations without trend, and 97.5% of the province indicated the increasing trend of temperature. The correlation between NDVI and rainfall indicated that in 81.8% of the province area, there was a negative correlation between vegetation and rainfall, and about 1.8% of the province had no correlation with the trend of changes in vegetation with rainfall. Positive correlation between these two parameters also was found for 16.4% of the province. The relationship between temperature and vegetation indicates that the negative trend of this correlation is observed in 53.3% of the province's area. Also, about 11.8% of the province did not indicate any correlation between vegetation and temperature. The positive correlation between these two indices was found for 34.9% of the province, so that the temperature increased with increasing vegetation, of which in 13.3% of these areas the correlation was positive and significant. The correlation between temperature and rainfall also shows that there is a negative relationship between temperature and rainfall over 97.4% of the province, of which has a negative and significant correlation was observed for ​​24.5% of the province's area. Also, in 1% of the area of ​​the province, there was no correlation between these two parameters and in 1.6% of the total area of ​​the province, the relationship between rainfall and temperature was significant and positive.
 

Based on the results, increasing and decreasing trends in the vegetation and climatic parameters were variable in the different regions of study area. An increasing trend (about 64.1%) for vegetation was observed in the study area and mainly in agricultural land use. On the other hand, during this time period, the trend of climatic parameters such temperature and rainfall were increasing and decreasing until 2016, respectively. Therefore, more than half of Fars province is characterized by rainfall shortage and increased temperature, which ultimately would result in reduced water for agriculture. The analysis of the vegetation trend in Fars province showed that 22.3% of the study area had a negative trend during this 12-year period. These areas are mainly rangeland and forests. According to results, correlation analysis showed a negative correlation between NDVI and rainfall and a positive correlation between NDVI and temperature in these areas. Another reason for this negative correlation is the adaptation of plants to the region conditions which has made the plants more resistant to drought and water stress (Lamchin et al., 2018).

Rangelands are natural ecosystems having large genetic resources. Since plant vegetation is the bed of life on earth and changes under the influence of surrounding environmental elements, using environmental element can highly contribute to estimate vegetation percent more accurately. Two effective elements which can contribute to estimate the vegetation distribution are climatic elements and geomorphometric. Nowadays, one of new techniques which have attracted much attention to estimate vegetation percent is machine learning process which is able to establish a relationship between various variables of environmental conditions with the least costs and workforce. Therefore, in this study, geomorphometric and climatic elements and data mining techniques have been applied to address the vegetation percent.

The studied region is a part of Yazd-Ardakan plain and Nadoshan region. Sampling and measuring vegetation percent have been carried out in using transects and plots. In order to extract the geomorphometric elements, digital elevation models and SAGA software were utilized and also, seven meteorological stations were regarded to achieve the climatic elements. In the current research, to investigate the impact of different climatic and geomorphometric elements on vegetation percent estimate, data mining models such as artificial neural network, the nearest neighbor, support vector machine, decision tree, Gaussian process and linear regression were used.
Artificial neural network: is one of computational models which can determine the relationships between inputs and outputs of one physical system and a network of connected nodes even if they are complicated and nonlinear.
The nearest neighbor: involves selecting a certain number of data vectors and random sampling from the set-in order to simulate the time interval followed by a certain period.
Support vector machine: is an efficient learning system based on theory of optimization applying the inductive principle of structural error minimization which leads to a total optimum response.
Decision tree: is a method to estimate the discrete functions which are strong against the confused data and are capable to learn the terminology with two different fields.
Gaussian process: is a random one consisted of random values in each point in a time or location domain so that each random variable has a normal distribution.
Linear regression: is applied to model the value of a dependent quantitative variable based on a linear relationship with one or more independent variables.
To assess the models and compare the results, such assessment criteria as RMSE, correlation coefficient and coefficient of determination have been used. Here, to weigh the input parameters of support vector machine algorithm, normal vector coefficients related to a linear support vector machine were specified as the weights.

The study indicated that data mining models are able to estimate vegetation percent more accurately. Using geomorphometric elements, data mining models have shown that Gaussian process model had the most accuracy in the set of training and test data. As well, in applying the models on climatic data, it has been reported that decision tree had the most accuracy in the set of training and test data to estimate the vegetation percent.
 

Vegetation is controlled by such environmental variables as geomorphometry and climate. The results have indicated that geomorphometric elements are of more impact on vegetation percent prediction as compared to climatic ones. Weighing results showed that such geomorphometric elements as distance to waterway, waterway baseline and elevation and such climatic ones as humidity affecting the vegetation growth rate were of the highest weigh and impact in vegetation percent prediction in the desired region.

salinity is known as the most important inhibitor of seed germination of most plants and limits the establishment of plants in arid and semi-arid regions such as Iran. The first effects of salinity on plant growth are associated with reduced seed germination and lack of uniformity in plant emergence. Currently, identification and utilization of tolerant cultivars are one of the most important methods in exploiting and increasing the yield in dry and saline soils. Because of tolerance to drought stress and salinity, quinoa can produce seed in a zone of soil salinity, which wheat, barley or other crops are not able to produce.

In order to study the effect of nitrogen and phosphorus bio fertilizers on seed germination indices of quinoa (Sajama and Titicaca cultivars) under salinity stress, a factorial experiment was conducted in a completely randomized design with three replications in 2018. In this experiment, Sajama and Titicaca cultivars as the first factor, four levels of salinity stress (0, 4, 8 and 12 dS/m) as the second factor and four levels of biofertilizer including control, nitroxin, biophosphorus and the combination of nitroxin and biophosphorus as the third one factor was evaluated. In this research, some germination traits including germination percentage, germination rate, vigor index, primary shoot length, primary root length, primary root to primary shoot length ratio and Seedling fresh and dry weight were investigated.

The results of the experiment showed that both quinoa cultivars are highly adapted to salinity in the germination stage and application of bio fertilizers could increase the tolerance to salinity in both cultivars so that, except for germination rate in Sajama cultivar, all traits measured in both cultivars at salinity level of 4 dS/m increased significantly under the influence of biological fertilizers in both cultivars compared to the control treatment. By increasing the salinity level to 12 dS/m, the values of seed germination traits decreased. However, even at this salinity level, inoculation treatment with biofertilizers in both cultivars could increase the traits compared to non-biofertilizer treatment at the same salinity level. Among the two cultivars, Sajama cultivar had higher primary shoot length, primary root length and vigor index, but the percentage and rate of germination were higher in Titicaca cultivars. Totally, the results showed that seed germination of Titicaca cultivar under biophosphorus fertilizer treatment at salinity level 4 dS/m was more than other treatments, which indicated the higher level of tolerance in Titicaca cultivar to this level of salinity in the germination stage, and since one of the most sensitive steps against salt stress is the germination stage that affects the establishment and optimum plant density. Titicaca cultivar can be recommended as a promising cultivar with high yield potential, which has high yield in terms of saline agronomic conditions, as well as high quality products for cultivation in arid and salty areas.

The ecological, economic, and social potential of an area for large and large uses is influenced by the quantity and quality of the waters. Therefore, appropriate methods of surface water and groundwater have been investigated qualitatively and quantitatively in order to use its results in assessing the power of the land. One of the important factors for sustainable development is the availability water resources for different uses, which imposed its quality is very important. Nowadays, groundwater resource management plays the main role in arid and semiarid regions. Investigations on the spatial variations of Water Quality Index (WQI) are very important to determine the best management program. Geo-statistical methods and ArcGIS software can be useful for this purpose. The aim of this study is WQI zoning for various uses (drinking, agriculture and industry) in the Silveh watershed (West Azerbaijan province).

In this research, water quality zoning based on WQI method for different drinking uses (pH, TDS, HCO3, Cl, Ca, Mg, K, Na and SO4), agriculture (EC, SSP and Cl) and industry (pH, TDS, Cl, TH and SO4) were sampled from 145 points (springs) from the basin level representing the studied area, which was carried out in July 2012. Then, to measure the parameters, the samples were transferred to the laboratory of the Faculty of Natural Resources of Tehran University and tested and the parameters were measured. In this research, we try to compare different methods of interpolation and select the best method to base the groundwater quality zonation map using the WQI index. Initially, the WQI index was calculated for all sampling points. After calculating the water quality index, the zoning water quality in the area was used Inverse Distance Weighting, Global Polynomial Interpolation, Local Polynomial Interpolation, Radial Basis Function and Kriging methods. Geostatistical methods to evaluate and select the best method of ArcGIS is the ability to perform cross-validation techniques and statistical criteria Root Mean Square Error (RMSE) is used.

Water Quality Index (WQI) zoning in the Silveh watershed for various uses showed that this area has no limitations in terms of groundwater quality and the use of groundwater for drinking, agricultural and industrial uses. The lowest and highest water quality index for drinking water in the area was 33.08 and 38.67, respectively, which is in the high-class of water quality index (less than 50). The lowest and the highest water quality index for agricultural consumption in the area was 36.27 and 72.77, respectively, which is in the high and good class of water quality index (less than 50 and 100-100), and There is no limit to agricultural consumption. Also, the lowest and the highest water quality index for industry consumption in the area was 34.32 and 64.96, respectively, which is in the excellent and good water quality index (less than 50 and 100-100) respectively and for the unlimited use of the industry.
 

Based on mountainous conditions and limited human activities (except for the southern and eastern areas of agricultural activities), the quality of water in the area is good. Although it is growing from the southeastern part of the city of Piranshahr, measures should be taken to prevent degradation of groundwater quality in the area due to various activities. Land use surveys show that agricultural use is about 19.97%, good rangelands are about 50.66%, good poor rangelands, 6.33% of the basin, and county of Birland and residential land cover 23.27% and 34% of the area respectively. The results also clearly illustrate the distinction of land use in the catchment area, because the water quality index in the agricultural and vegetation areas is in a good class (100-150). Given that 50% of the area is covered with good pastures (these rangelands do well to clean up underground water). The results of this study confirm the field performance in terms of high performance and water quality indexes, and recommends that similar research be used. Of course, the lithology of the area should not be ignored. Because water quality has a high correlation with the region's mineralogy. The existence of calcareous and dolomitic stones in the studied area is also a reason for the good water quality of the area.

Increasing or reducing the production of suspended particles depends on the important climatic and terrestrial characteristics of a region. One of the most important climatic factors affecting soil erosion is wind speed, so if the surface winds speed in a region exceeds the threshold of erosion, soil susceptibility to erosion and dust production increases. In contrast, the most important factor controlling soil erosion is soil moisture content, which increase soil stickiness and decrease soil susceptibility to wind erosion. The various researches have been conducted on the relationship between climate parameters and Horizontal Visibility (HV), while the relationship between these variables has not been explored precisely at the occurrence time of dust events. For this reason, this study attempts to investigate this relationship on a daily scale. Given that the measurement of soil moisture in laboratory conditions or during land operations, especially in large scale is time and cost-consuming; to overcome this problem, we were used SMAP satellite images in the present study for a 2-year period (April 2015-April 2017).

The aim of this study was to investigate the correlation between horizontal visibility with wind speed and soil moisture content retrieved from SMAP satellite imagery for southeastern regions of Iran. For this purpose, hourly data related to horizontal visibility and wind speed were obtained from the Iranian Meteorological Organization. Soil moisture content was retrieved from the SMAP satellite data. Since the SMAP satellite has 3-day spatial resolution and the main purpose of the present study is to examine the role of wind speed and soil moisture on the day of the occurrence of local-source dust; Therefore, in the next step, the dusty days that have the information related to Horizontal visibility, wind speed, and soil moisture content, were extracted. The pre-processing of SMAP satellite data was done based on the theoretical basis algorithm. Then, soil moisture content was retrieved at the site of the meteorological stations. Finally, the relationships between independent (soil moisture and wind speed) and dependent variables (HV) and as well as, the determination of the relative importance of each of the independent variables were analyzed based on ridge regression method.

The results showed that in recent years, the trend of daily changes in the horizontal visibility caused by sand and dust storms in south-eastern Iran has been decreasing. The results of Ridge regression analysis indicated that there was a significant and inverse relationship between the velocity of surface winds and the horizontal visibility, as well as a significant direct relationship between soil moisture content and horizontal visibility at Zahedan and Zabol stations. The correlation coefficient of Ridge regression model for these two stations was estimated to be 0.52 and 0.44, respectively. In Iranshahr, the relationship between the velocity of surface winds and the horizontal visibility was positive and significant, while the relationship between soil moisture fluctuations and visibility was non-significant. Horizontal visibility change due to the increase in wind speeds in this region was estimated at 35%.
 

The findings of this study proved the effective role of increasing wind speed and decreasing moisture content of soil surface in decreasing horizontal visibility and intensifying air pollution in southeastern Iran, especially in Zahedan and Zabol cities. The results indicate an exacerbation of the phenomenon of desertification due to the occurrence of a destructive phenomenon of wind erosion in southeastern Iran. In other words, the ability of self-organizing the land of this area from the Iran has decreased and led to the expansion of the ecosystem of the desert, the increase of the occurrence of dust phenomena and reduction of HV in the study area. In fact, these results point to the drop in the threshold speed of wind erosion and the easier lifting of soil particles by surface winds. Therefore, if proper management and enforcement measures are not taken to control this destructive environmental phenomenon, the sustainability level of ecosystems in these areas is expected to decline sharply, and even these ecosystems may be irreversible. As a result, its economic and social damages will affect the ecosystems and people of these areas more than in the past.