فصلنامه پژوهش های فرسایش محیطی
سال دوازدهم شماره 2 (پیاپی 46، تابستان 1401)

Acacia trees, whose leaves and fruits are used for livestock, are a bio accumulator of heavy metals and although they play an important role in absorbing heavy metals from the soil, their fruits are used for livestock consumption. Prosopis trees with their long and strong roots have the ability to absorb heavy metals from the environment and researchers use them as biomarkers for heavy metals as well as phytoremediation. Although phytoremediation is a good way to remove heavy metals from the environment, it cannot be a good way to protect the environment if the plant is a source of nutrition for living organisms. In arid and semi-arid regions of Hormozgan province, the plant organs of Prosopis, Acacia and Ziziphus trees, especially their leaves and fruits, are considered as the food chain of local and wild cattle in the region. If these plants are contaminated with heavy metals and their toxicity is high, they endanger living organisms. Nevertheless, the amount of heavy metal contamination in these plants has not been identified and experts do not have information in this field to be able to develop a plan to control it. If these trees are contaminated with heavy metals, it is not clear how the contaminant accumulated in the plant and the sources of its spread are not known. For this reason, the present study tries to answer these leading questions and it is assumed that air pollution, especially desert dust and road dust is the main cause of heavy metal accumulation in trees. Therefore, the main purpose of this study was to detect the concentration of heavy metals in the leaves of tree species through atmospheric dust and the intended experiments were carried out in the natural forests of Kahouristan region.

In order to achieve the objectives of the study on identifying the concentration of heavy metals in tree species in arid areas of Bandar Abbas, first the location and population of the experiment was determined. The test site is in Kahouristan region -90 km northwest of Bandar Abbas- in Hormozgan province with a geographical position of 27 degrees and 17 minutes north latitude and 55 degrees and 21 minutes east longitude with an altitude range of 250 to 320 meters above sea level. The experiment was conducted in the spring and summer of 2020. The experimental population was selected in a randomized complete block design. The community contains 180 trees, including the species Acacia, Ziziphus and Prosopis. 60 trees were considered from each tree species. On these trees, three treatments were considered in such a way that each treatment included 60 pieces, of which 20 trees were considered for each treatment: 1-Treatment of road dust and desert dust in Acacia, Ziziphus and Prosopis trees located near the Bandar Abbas-Lar transportation road and solid particles emitted from the exhaust of vehicles are deposited on the leaves and soil substrate of the plant and absorb the roots of the trees. 2- Treatment of desert dust that is far from the road and only desert dust settles on the soil and leaves of trees. 3- Control treatment which in terms of distance is far from the road, after the treatment of desert dust and the trees in this treatment are located in the mountain shelter and away from the path of desert dust. Each treatment contained 60 pieces, of which 20 were related to Acacia, 20 pieces of Ziziphus and 20 pieces of Prosopis. Therefore, 180 specimens containing three tree species (blocks) and three treatments formed the experimental community. After introducing the experimental population and identifying the treatments and tree species, the concentrations of heavy metals in the leaves of the experimental treatments and trees were measured. The measurements were taken in two seasons, spring and summer, when desert dust occurs. From each tree, 5 leaves were sampled and transferred to the laboratory after labeling. In the laboratory, the concentrations of heavy metals in leaves including lead (Pb), cadmium (Cd), zinc (Zn) and manganese (Mn) were measured.

 Statistical analysis of heavy metal concentrations in the experimental population shows that the average concentration of lead, manganese and cadmium in the experimental population is higher than the standard limits in the plant, but the concentration of zinc is less than the allowable limit. The concentration of zinc in the experimental community is such that the average is 209 mg/kg, the standard in the plant is 100 to 400 mg/kg, but its distribution in the experimental community is normal. The maximum accumulation in the leaves of dust treatment is due to road transport and the fume that is emitted from car exhaust and its average in this treatment is between 300 to 400 mg/kg, which is close to the upper limit of the standard of this element in a plant. In the treatment of desert dust, the concentration of zinc is between 180 to 220 mg/kg, which in honeysuckle species has a higher concentration than acacia and Ziziphus. The lowest concentration of zinc in the control treatment was between 70 and 80 mg/kg and even lower than the plant standard. Statistical analysis of lead concentration in the experimental community showed that its average was 35 mg/kg, which is higher than the permissible limit in the plant. The highest concentration of lead was in the trees near the road and in the treatment of dust caused by road transport and its deposition on the leaves and soil substrate of plant species and its concentration was between 50 to 60 mg/kg. The maximum was in the species of Prosopis, which has accumulated more lead metal than acacia and Ziziphus. In the treatment of desert dust and its deposition on the surface of leaves and soil of trees, the concentration of lead was between 25 to 40 mg/kg and was in the second place, and among the tree species, Prosopis has a greater ability to withstand the adsorption and accumulation of lead metal compared to acacia and Ziziphus. The average of manganese metal is 115 mg/kg, which is higher than the permissible limit in the plant (15 to 100 mg kg). The highest concentration of manganese in the dust treatment was due to road transport and its concentration was between 145 to 155 mg/kg, the maximum of which was in the species of Prosopis, which is more concentrated than acacia and Ziziphus, manganese metal. Manganese concentrations are close to each other between road dust treatment and desert dust, but their differences with the control are very noticeable. The standard range of cadmium is between 0.2 and 0.8 mg/kg, the average of which in the whole study population is 2.93 mg/kg, with a maximum of 13.5 mg/kg, and at least 0.02 mg/kg in control treatment. Comparison of the mean of treatments showed that the highest concentration of heavy metals in the dust treatment was due to road transport and the concentration of metals in this treatment was different from other treatments and was in the first category. Trees affected by desert dust were in the second category in terms of heavy metal concentrations and the difference with other treatments was significant. Finally, the control trees have the lowest concentration of heavy metals and are located in the last floor. In the treatment of road dust particles, the concentration of all heavy metals was higher than the standard range of metals in the plant. In the treatment of desert dust, the metals of lead, manganese and cadmium were higher than the standard; however, in control trees, the concentration of lead was higher than the standard and the concentration of other heavy metals was less than the standard. The highest concentration of heavy metals was in the Prosopis tree species, but in Acacia and Ziziphus tree species, the concentrations of heavy metals were not statistically different and were very close to each other.

 The results of this study showed that the trees along the Bandar Abbas-Lar Road have been exposed to toxic dust from vehicles. Farther from the road, trees, although far from road dust, are exposed to atmospheric dust from industry as well as dust storms. The concentration of heavy metals in the leaves of roadside trees and then exposed to fine dust is much higher than the standard. Also, among the species, Prosopis trees, due to their long and strong roots, have the ability to absorb heavy metals from the soil and in the leaves of these trees, especially on the roadside and exposed to fine dust, cadmium concentration, lead and manganese are very high and cause heavy metals to be removed from the environment, but the leaves and fruits of Ziziphus, Prosopis and Acacia trees are one of the important food sources for cattle and animals and even birds of Kahouristan region. The accumulation of heavy metals in the limbs of these trees and their transfer from plants to animals and eventually humans is a threat to ecology. Therefore, it can be concluded that toxic road dusts and atmospheric dust have caused the deposition of heavy metals in the soil bed of annual and herbaceous plants and trees of the region, which are absorbed through the roots of the plant and thus enter the food cycle. It becomes a trap for the existing living and nonliving species and human beings. The results of this study showed heavy metal pollution in the dry ecosystems of Kahouristan area and the results can be made available to environmental and health experts. In addition, it is suggested that the concentration of heavy metals in annual herbaceous plants, which are the food of local livestock in the region, be evaluated in order to find a way to control their spread and contamination of environment.

The destructive phenomenon of desertification, in addition to land degradation, and causing environmental problems and dust events, exert great damages to human societies such as damage to transmission lines, and blockage of roads and railways. Various mechanical, chemical, and biological methods have been proposed to deal with desertification. Recently, in different parts of the world and some parts of Iran, biological soil crusts have been used as biological improvers to modify and stabilize the soil against desertification. Biocrusts consits of a collection of lichens, mosses, algae, fungi, bacteria, and cyanobacteria that play a major role in soil regeneration, increasing desert ecosystem performance, and combating desertification. Researchers have identified the addition of cyanobacteria to the soil as an effective way to improve soil and increase soil ecosystem performance, especially in desert areas. For this purpose, cyanobacteria are used alone or in combination with plant cultivation.

The study area is part of the Sejzi Desert (Central Deserts of Iran) which is located in Isfahan province of Iran. Wind erosion threshold speed was measured in Sajzi plain using a portable wind tunnel. After plotting the storm rose by the data obtained from the synoptic station of Shahid Beheshti Airport in a period of 25 years from 1991 to 2016 by using WR Plot 7 software in different seasons, the speed and frequency of winds were determined. For conducting the intended experiments, 4 soil samples were collected from the area with biocrust cover and 4 other samples from the area without biocrust cover. PH, EC, OC, saturated moisture content, MWD, dry grain size distribution and soil texture were measured for soil samples. Carbohydrate monomers were identified by HPLC. For the culture of cyanobacteria, very small pieces of soil undercovered with cyanolichens were placed on BG 11 medium, and the processes of isolation, culture, and purification were done, respectively. After sieving the soil through a 4.75 mm, the wind tunnel trays in the dimensions of 30 x 50 x 8 cm with an area of 1500 cm2 were filled with soil and their surface was smoothed. The control trays were also saturated to a depth of one centimeter with a distilled water. The amplified cyanobacteria were then isolated from the culture medium under a BX51 light microscope and prepared in a water solution with a biomass weight of 2.5 g/l by inoculated water method and in the required amount according to the pore volume and were sprayed on soil samples. Mass fluxes were measured for all soil classes at different wind speeds according to the erosion threshold speed between 7.15 to 15.06 m/s. Shear strength and soil moisture were also measured. Experiments were performed completely in a randomized design in three replications.

The wind erosion threshold in the center of the Sejzi plain was 3.76 m/s on the sandy loam soil texture. Most wind erosion occurs in late winter and early spring. In spring, 35.8%, and in winter, 21.9% of winds had a speed of 7-11 m/s. The EC, sand, and silt were lower, in the soil undercovered biocrusts, but saturation moisture, MWD, clay, and OC were higher. Of the four identified monosaccharides, arabinose was found in all three samples, but its amount was different in soil samples. Also, Mannose and Xylose were identified and measured in only one of the soil samples at 0.01% and 0.02%, respectively. Based on morphological characteristics, two specimens of cyanobacteria including Microcoleus vaginatus and Coleofasciculus chthonoplastes were identified. In samples treated with carbohydrates, there was no change of mass fluxes and threshold velocity of wind erosion, and shear strength with control samples. According to the results of the ANOVA test, the mean winding at different wind speeds was affected by soil moisture and texture and treatment with cyanobacteria. By speed of 7.15 and 15.06 m/s, mass fluxes had a significant relationship with cyanobacterial treatment and soil texture. At 11.21 m/s, sediment yield values were affected by texture classes and soil moisture content and had no significant relationship with cyanobacterial treatment. The results showed that the addition of cyanobacteria to the soil increases the shear strength of the soil against the wind force. Texture type and soil moisture percentage had no significant relationship with shear strength values.

The central parts of the Sejzi plain were sensitive to wind erosion in terms of soil characteristics and compared to its peripheral strip, wind erosion was more intense and had destroyed the plain soil. The presence of microorganisms in the soil and its successive proliferation increases the organic content of the soil and increase soil stability, while adding a certain amount of carbohydrates artificially in the soil surface layer, especially in natural resources, leads to their decomposition by environmental conditions and climates such as ultraviolet rays and their function in the soil is reduced and may even stop. Cyanobacteria are better established in loam and silty loam soils than other types of soil and this has affected the soil loss flux, but due to the sensitivity of particles smaller than 0.84 mm to wind erosion of more particles at a speed of 11.21 m/s, cyanobacterial treatment was significant. The difference between the samples treated with cyanobacteria was due to the resistance of fine particles between the filaments of cyanobacteria Microcoleus vaginatus and Coleofasciculus chthonoplastes. According to the USDA classification, the range of soil particles for Fine Sand and Very Fine Sand varies from 0.25 to 0.05 mm and are suitable for stabilization with Microcoleus vaginatus and Coleofasciculus chthonoplastes cyanobacteria.

Vegetation is the first and the most important producer of any ecosystem and reflects many factors in the ecosystem, so by studying the relationship between its changes and other factors such as dust, the interaction of factors can be understood. The purpose of this study was to investigate the effect of vegetation cover and its relationship with the occurrence of optical depth of air (AOD) caused by dust events in Hormozgan province during the study period of 2000 to 2020 by using NDVI, SAVI vegetation indexes and other climatic variables.

The present research investigated the relationship between dust and vegetation. For this purpose, aerosol optical depth (AOD), which is one of the most widely-used indicators for analysis and monitoring of suspended particles in the atmosphere, was used. To determine the dust areas, the AOD index was normalized; then the numerical value of AOD> 0.5 was considered as the high amount of dust. For studying the trend of AOD change, by coding in the Google Earth Engine environment(GEE), the image of MODIS dust products from 2000 to 2020 (20-year period) was extracted and subsequently the dust time series diagram was prepared. According to this chart, the month and year that had the highest level of dust during this period were determined. The NDVI and SAVI indexes during the years (2000-2020) were coded for an average of three months (June, July and August) in the Google Earth engine environment and all satellite images from Landsat 5, 7 and 8 sensors were extracted with a resolution of 30 meters. Then the correlation between vegetation indices and climatic variables with AOD index were calculated.

The results showed that June, July and August had the highest AOD index during the study period and the lowest level was in December of each year. The largest AOD occurred in July during the study period. The most severe case occurred in July 2018 with a magnitude of 0.69 and the lowest case was in December 2000. The charts illustrated that the largest level of dusts occurred in 2003, 2011 and 2018 and the lowest levels occurred in 2000, 2002 and 2017 in Hormozgan province. The results showed that, among the climatic indicators, evapotranspiration, minimum temperature and wind speed had the highest correlation with AOD index. Also, from 2000 to 2020, the rate of evapotranspiration and temperature increased with a slight slope, and due to the direct relationship between these factors and the AOD index, the AOD index increased during the period. The results of correlation between SAVI and AOD showed that there was a slight or weak relationship between them and the correlation index between NDVI and AOD showed a moderate to strong relationship.

The results of the study indicated that the changes in the AOD index in the twenty-year period (2000-2000) have no significant trend and have been mostly influenced by climatic factors and vegetation density in Hormozgan province. Also, the results of studying the spatial pattern of AOD in the province showed that the distribution of AOD in the south to south-west is more than the south-eastern regions of the province. The results showed that the condition of surface vegetation was significantly related to the amount of AOD and their levels increased with decreasing vegetation. Studies have shown that in areas of the province where vegetation is sparse and barren compared to other parts of the province more AOD exist. Also in years when vegetation is less dense due to reduced rainfall, the intensity of dust has increased in these years. The results showed that 2018, 2008 and 2003 years showed the dustiest days and 2000 and 2017 showed the least dusty days per year. According to the results, the highest amount of AOD occurred in the south and southwest of Hormozgan province, which matched the results of NDVI index in these areas according to which vegetation is in the class of 0.1-0 (without cover). This confirms the results of the relationship between dust and vegetation.

Dust is a mass of micron-scale solid particle that is dispersened into the air and has many harmful environmental, social and economic effects. Sarakhs city is one of the critical centers of wind erosion and dust in the country, which is affected by the 120-day winds of Sistan and no specific trustee has been observed for the scientific and practical study of this phenomenon in the region. In order to know the texture characteristics of this region with the aim of identifying the origin and sedimentological characteristics of dust particles, dust samples were collected in two consecutive months (August-October 2019) in Sarakhs region. The samples were subjected to laser analysis (LPSA) by Grad state software and electron microscopy (SEM) using Anix Emica software. In the sample of villages from Gonbadli to Baghboo, the amount and percentage of silt is 96, 92, 89, 99 and 96 and the clay content was less than 5% of the particles. Electron microscope output showed that the particles were irregular as a result of surface degradation of the grain so that, as a result of particle collision or dissolution, part of the grain is destroyed and the particle shapes are indistinct and polygonal Semi-circular to circular polyhedra. In some places, the surface of the grain was dirty and spotted or was in the form of flakes. Due to the different physical properties and the multiplicity of peak points in the particle size distribution diagram, most dust deposits in the region have multi-source characteristics. The prevailing wind direction of the northwest to southeast was also identified.

At first, the study section was selected with the help of experts and villagers and exisitng library studies of the villages that have mostly faced with and expereinced the dust crisis. Rural houses were established at a height of 2-4 m. Atmospheric dust was collected  by using 30 glass samplers for two months from August to October 2019. After collection and preparation, the samples were delivered to the Particle Laser Analysis (LPSA) Laboratory and Electro Microscopy(SEM) scan were performed.
The results of particle size ere analyzed by using Gradl State software and the output from SEM was analyzed using Anix Emica software. Particle size samples (particle size determination) had been determined by the manufacturer: France Cordouan with Vasco3 model SEM samples were analyzed by electron microscopy with magnification of 2-5000 and EHT20 KV.
Statistical parameters including sorting and mean skewness parameters and Kurtsis were calculated using Gradl State software.

In this study, first, the results of texture and granulation of suspended sediments deposited in different sedimentation systems in different parts of Sarakhs region were investigated.The results of granulation showed that silt has the largest share in suspended sediments and their texture is mainly in the middle range and forms particles less than 4 microns, these results were consistent with the findings of Salahi (2019) And Roghani (2018) .According to the results obtained from SEM, the shapes are hexagonal, and calcite crystals are well identified, indicating that the region contains clay silicates and carbonates as the predominant mineral of the quartz and calcite region. The results of the SEM showed that the Darta angle particles and are circular
The results of PSA showed that the particles have two or more peaks, which indicates two or more peaks that indicate multiple origins (Hosseini, 2021). The particle size results indicate that the particles are very well sorted and in a way confirm the close transport distance to the particle from the harvest area to the place where the particles are trapped in the dust traps. As we move from Bazangan village to Baghbo village, which is in the south of Sarakhs region, sediments with much better sorting and inclination towards finer-grained sediments are included, which indicates that the origin of walnut particles of dust covers a longer distanceIt is generally stated that the sensitivity in the region is very high due to the percentage of silt frequency which is very high in the region. For dust, several sources can be considered, including local origin related to the soils of the region due to quartz and calcite minerals and human origin due to particle size outputs and descriptive statistics (sorting, Skewness, and Kurtsis) and SEM, which show very varied particle dimensions. This indicates that the wind speed from northwest to southeast has been decreasing, with the particle size tending to become smaller.

The results of PSA are related to the samples taken from the villages in question from Gonbadli to Baghbghoo. The amount Percentage of silt 96% 92% 89% and 99%. and the amount of clay is less than 5% of the particles. The distance of the particles from the medium grain silt to the clay is included, indicating that the particles have carried a medium to long distance and have a source of wind erosion. According to the results of SEM obtained from the analysis of dust particles by electron microscopy, the particles are irregular as a result of surface degradation of the grain to the extent that it may be damaged as a result of particle collision or dissolution of part of the grain. In addition, the result of mechanical destruction or dissolution of the grain surface is seen in the form of small and large cavities. Scaling or surface scratches caused by the physical destruction of the wind or the action of dissolution on the grain surface cause grooves to be formed or a part of the grain surface to be separateed in the form of flakes and dusty grains. The particles are observed in some grains and even the surface of the seeds is often abraded and the dust does not have a special radiance. In the parts that are a little brighter, the dust is seen as light spots due to the impact of light. Also, in the observed output, in some places, they can be seen on surface of the dirty and spotted grain (such as the effect of a needle on a body) or in the form of scales and flakes so that this surface causes the scattering of light in different directions and sometimes its reflection causes the grain not to be well-defined.

In recent years, Minab plain, one of the most important agricultural plains in the south of the country, has experienced extensive land subsidence. Also, the study of the groundwater level of the Minab plain in a long-term periods showed that since 2001, the drop in the aquifer level has intensified and an average annual drop of 42 cm has been observed. Furthermore, previous geotechnical studies in the region have shown that besides the drop in groundwater level, the presence of swollen  and soluble sediments has increased the severity of subsidence in the Minab plain. According to the evidences of the previous studies and damages caused by land subsidence to infrastructure, buildings and agricultural lands, recognizing this phenomenon, identifying areas sensitive to it and investigating the factors affecting its occurrence can play an important and effective role in predicting land subsidence and preventing associated damages. In the current study, unlike other land subsidence studies, which emphasized the drop in groundwater level and the type of geological formations, the characteristics of soil and land surface in the explanation of subsidence phenomenon have been considered.

The study area of ​​Minab plain with an area of ​​653.6 square kilometers is located between the longitudes of 56° 49' to 57° l5' East and latitudes of 27° 1' to 27° 19' North. First, a time-series analysis of Sentinel-1 radar was performed to identify the land subsidence sites and calculate the displacement rate. After calculating the vertical displacement, for the spatial modeling and the preparation of land subsidence hazard map of Minab plain, data mining methods were employed with soil and land surface features. The investigated time period in the present study covers the years 2015, 2017 and 2019. Spatial variations of 12 variables including soil texture, vegetation, percentages of sand, silt and clay, land surface temperature, soil bulk density, land cover type and soil salinity were gathered and prepared using Google Earth Engine (GEE) and TerrSet software to be used as independent variables of the spatial model. Then, by using Naïve Bayes (NB), Decision Tree (DT) and k-Nearest Neighbor (kNN) data mining models in the RapidMiner software platform, the necessary information was extracted to map the potential land subsidence zones. It is noteworthy that in the constructed models, the land subsidence map for different years (resulting from Sentinel-1 radar image processing) was considered as a dependent variable and the maps accociated with 12 variables mentioned were considered as independent variables. Performance criteria such as classification error, kappa coefficient, absolute error, normalized absolute error, relative error, and root mean squared error were used to evaluate the resulting models with respect to spatial accuracy.

The amount of subsidence for 2015, 2017 and 2019 is computed 3.61, 0.92 and 5.69 cm, respectively. The results obtained by interferometry processes showed that the land subsidence progresses from the central parts of the plain to the edges of the plain and the eastern areas of Minab plain have less amount of land subsidence compared to other areas. Overlap of sinkhole points recorded in the field survey also showed good agreement with the results of radar image analysis. The highest rate of land subsidence, which was equal to 5.69 cm, occurred in 2019. This amount of land subsidence is certainly hazardous for agriculture, the environment and the facilities in the study region.

Among the independent variables, vegetation, heavy soil texture and soil salinity had a more meaningful effect on different land subsidence classes. Also, among the models used for land subsidence risk mapping, the performance of the decision tree method with an accuracy of 63.15, classification error of 36.85, kappa of 29.7, absolute error and relative error of 0.5, normalized absolute error of 0.45 and the sum of the squares error of 0.56 was better than the other two models. Findings of this study showed that soil and land surface characteristics have the ability to express 0.6 of the variance of land subsidence phenomenon in the region and for more accurate modeling, effective data such as changes in groundwater level and the geological material of aquifer can be used. Also, the findings of this study as well as the prepared map of potential land subsidence occurrence in different parts of the study area can play an important role in risk reduction, land use planning and water resources management in the region.

Depth to wet front is generally considered as the amount of water which penetrates into soil and wets the internal soil layer. This is an important variable especially in applications such as runoff generation and sediment yield estimation. This variable in some cases is used as a hydrological science in the form of a proxy for infiltration as an important factor in soil erosion processes. The importance and necessity of this research is estimating the performance of suspended sediment by adding the wetting depth variable to the regional model. The aim of this study was to investigate the effect of wetting depth on runoff and sediment production capacity by using the modified Williams equation.

In this field study, a rain simulator equipped with drip systems installed on a test site on a mountain slope to produce rainfall with intensities of 45 and 60 mm per hour on three slopes of 10, 20 and 30% was used with three replications. Adjacent to the experimental plots, soil surface depth was used to determine the physical and chemical properties of the soil, including soil texture, lime percentage, organic matter, initial moisture and aggregate stability. After setting the artificial sprinkler on the experimental plot, runoff and sediment obtained from each plot were collected in special bottles at 10-minute intervals and transferred to the laboratory. Then the amount of sediment produced at the end of each precipitation was weighed and calculated for each plot. Then, to measure the wetting depth using thin tissue rods, the wetting depth was measured every 10 minutes in diameter with 3 repetitions inside the plot. Explanation coefficient (R2) and Nash-Sutcliffe return (Nse) were used as sediment yield criteria for the model.

According to the results obtained in the study area and the efficiency coefficient, before adding the wetting depth parameter, the effect of precipitation intensity on sediment indicates a significant difference. The Nash coefficients at intensities of 45 and 60 mm/h are 0.84 and 0.63%, respectively, while the Nash coefficient for the combined intensities (total) of 45- and 60 mm/h is 0.55. Due to the low Nash coefficient in the Williams equation, for investigating the wetting depth on the amount of sediment, the Williams equation was modified. Based on the results in the study area, the relationship between wetting depth and the amount of suspended sediment in the mentioned intensities and slopes through the equation of Qs=(qe×qpe)0/32×S0/28×1/17dp×DA2/89 was obtained. Sediment performance has a non-linear and inverse relationship with wetting depth at the desired intensities and slopes, and also with the parameters of slope, runoff volume, runoff peak discharge and area. The results showed that the extent that increasing the slope, runoff, runoff peak and area leads to increasing the amount of sediment, and increasing the wetting depth reduces the amount of suspended sediment. The calibration and validation results of estimating the amount of suspended sediment in the developed model showed the value of Nse= 0.81; R2 = 0.81.

This study investigated the effect of wetting depth on runoff and sediment production capacity in the field at slopes of 10, 20 and 30% with rainfall intensities of 45 and 60 mm/h using the modified Williams equation. In the present case without considering the wetting depth variable, the results showed that at intensities of 45 and 60 mm/h, the amount of sediment and runoff increases by increasing the slope. This is because the shear energy poured on the soil by raindrops has produced runoff and sediment. But by combining rainfall intensities of 45 and 60 mm per hour, the amount of Nash coefficient decreased. One of the reasons for this is the increase in the diameter of raindrops and also the increase in the number of droplets that hit the soil surface. Although increasing the intensity of rainfall in most soils increases sediment by increasing runoff, it can sometimes reduce sediment efficiency by reducing sediment due to the spatial homogeneity of the slope of the soil surface layer. Addition of slope and wetting depth parameter, which is due to the amount of infiltration that moistens the soil around the bottom layers, is one of the factors that affect the production of sediment. The results of evaluating the efficiency of the regional model showed that the equation obtained for the region fits well with the observational data to the extent that the coefficient of explanation of fitted lines on the data increased by adding the wetting depth variable. There is an inverse exponential relationship between the amount of leached sediment and the wetting depth. Performance coefficient indices such as sedimentation coefficient and explanation coefficient, as two of the most widely used indicators to evaluate the results in the simulation of the amount of sediment, showed the value of Nse = 0.88 and R2 = 0.88. The results of estimating the amount of suspended sediment in the calibration (Nse = 0.81; R2 = 0.81) and validation (Nse = 0.81; R2 = 0.81) stages were very suitable for the study area.

In geomorphological studies, it is important to prepare landforms for the study of forms in different regions. In the same vein, with more accurate input data, landform maps are prepared with higher accuracy. Therefore, by using digital elevation model maps with more resolution, more accurate landforms can be extracted (Shayan et al., 2005). Identifying landforms, classifying them, and identifying different geomorphic forms are important in examining the relationships between form and process in the area. By extracting landforms, various information such as climatic characteristics, soil type, and hydrology can be estimated in a watershed. Due to the significance of the issue, it is important to use a digital elevation model with more resolution to prepare landforms with more accuracy. There are several methods to increase the spatial resolution of the digital elevation model. Obtaining more detail from pixels was first proposed by the Gravity Model by Atkinson (1977). In this technique, the pixels are divided into several sub-pixels according to the values ​​of the neighboring pixels. In the gravity method, a large pixel is subdivided into sub-pixels, and a ground cover class is assigned to each sub-pixel. There is a limitation that the total number of sub-pixels of each class is directly proportional to the percentage of canopy coverage of the larger original pixel (Atkinson et al, 1997). In this way, soft input layers can be converted to hard categories with better resolution. The main problem in sub-pixel mapping is determining the location of each land cover class in larger pixels (Verhoeye, 2002). Various methods have been proposed to solve this problem, including the Hopfield network (Tatem et al., 2001; Muad and Foody 2012), the neural network after error propagation (Zhang et al., 2008; Wu et al. 2011, Nigussie et al. , 2011), linear optimization technique (Tatem et al., 2001), spatial gravity model (Mertens et al., 2006; Wang et al., 2011), pixel displacement algorithm (Kasetkasem, 2005), and genetic algorithm (Mertens et al., 2003).

Gravity model
In this model, the pixels in the digital model of altitude are named based on their position relative to the upper left pixel, known as P0.0. The same structure is used for subpixels. This means that for a scale equal to 2, it has sub-pixels p0,0, p0,1, p1,0 p1,1. So that a sub-pixel pa, b is placed inside a pixel Pi, j when the following equation is established (Xu et al., 2014):pa;b∈Pi;j⇔(aS=i)∧(bS=j)Where a is the sub-pixel row number, b is the corresponding sub-pixel column number, s is the scale factor, and i is the neighboring pixel row number, and j is the neighboring pixel column number. The neighborhoods defined in the previous step are also defined as follows:N2pa;b=Pi;j|d(pa;b.Pi;j)≤12(2S-1)Where N2 is a quadruple neighborhood model. The distance between each sub-pixel and the surrounding pixel (d) is calculated as follows (Xu et al., 2014):dpa;b.Pi;j=a+0.5-Si+0.52+b+0.5-Sj+0.52Topographic Position Index (TPI) method for landform extractionIn this study, the neighborhood method was used to study and classify landforms. Thus, the topographic position index (TPI) was used to isolate landforms in the region. TPI is the equation of each cell in a digital elevation model with the average height of neighboring cells according to the following equation. At the end of the height, the average decreases from the height in the center (Weiss, 2001).TPIi=Z0-∑n-1Zn/nZ0 is the height of the model point under evaluation, Zn is the height of the grid and n is the total number of surrounding points considered in the evaluation.

In this study, to increase the spatial resolution of the digital elevation model of southern part of Fars province, the gravity model was studied. First, the gravity model was used to increase the spatial resolution of the 30-meter DEM. In this study, four neighborhoods with different scales 2, 3 and 4 were used to find the best model to increase the spatial resolution. The results showed that the use of quadratic neighborhood (T2) with scale 2 increases the number of sub-pixels and increases the spatial resolution. According to the error values, it is determined that the best model to increase the spatial resolution is the model S = 3 for the digital model of 30 meters height. Therefore, digital elevation (DEM) model S = 3 and T = 2 were used to map the landforms of the region as input data. TPI method was used to extract the landform map of the study area. The results of applying a polynomial distribution function to select the best scale for landforms separation showed that 3 × 3 (minimum scale) and 45 45 45 (maximum scale) windows with the lowest RMSE for TPI mapping and finally landform mapping were the most suitable ones in the study area. The results showed that the TPI values ​​of the study area are between -33 to 46.77 for the 3 3 3 scale and -42.53 to 77.56 for the 45 45 45 scale (Figure 4). Indeed, in high areas such as ridges and hills, near-zero codes indicate flat areas or areas with low slope changes, and negative codes indicate low areas such as valleys and waterways. Each of the categorized landforms covers a part of the area. According to the results, it is clear that the study area includes 10 types of landforms. The results also show that the map of landforms prepared using the gravity model is more accurate.

In this study, gravity model and TPI method were used to study landforms in the south of Fars province. In this study, the resolution of images was increased using the gravity model. The results of this study showed that the gravity model with scale 3 and quadruple neighborhood has a high accuracy to increase the spatial resolution of the digital elevation model. Therefore, by using these maps with high spatial resolution, landform maps can be prepared with high accuracy. Also, by using the type of landforms and their percentage, the erosion rate in the study area can be estimated.

       Satellite data is one of the fastest and the least expensive methods available to researchers to prepare land use maps (Pal and Mather, 2005). Analysis of this data can provide accurate insights into human interaction with the natural environment. In particular, the use of multispectral image analysis can help humans identify land cover (Brian and Michael, 2005). The use of different parts of the magnetic energy spectrum to record the properties of phenomena and the possibility of using hardware and software have made the use of satellite images particularly popular (Richard and Jia, 2006). In general, classification methods can be divided into supervised and unsupervised methods (Ommen, 2008). In the monitored method, we can refer to the maximum probability methods, the minimum distance from the mean, the Mahalanui distance, the parallelogram, the neural network and the support vector machine. In fact, the classification process is the conversion of data into comprehensible information (Rakis, 2011). The maximum probability method is one of the most efficient methods of classifying images (Jensen, 2005). In most research studies, this method has been introduced as the most accurate classification method (Riahi Bakhtiari, 2000; Hovang and Tonshend, 2002). In this method, the user must be careful that the classification follows the normal Gaussian distribution, and this method is more suitable for multispectral classes.

Landsat 8 was launched on an Atlas-V rocket from Vandenberg Air Force Base, California on February 11, 2013. The satellite carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments. The capabilities and advantages of the OLI sensor compared to the ETM + Landsat sensor are as follows: better spectral resolution with narrower bandwidth ranges and 2 more spectral bands (obtaining information in 9 spectral bands), quadrupling the geodetic recording accuracy absolute images, changing the geometry of the image from Whisk broom to Push Broom and thus obtaining150 more information scenes per day (400 images per day), improving radiometric resolution from 8 bits to 12 bits and the possibility of better description of the ground cover and increasing the Signal Ratio to Noise (SNR) (Biranvand and hashim, 2015). In this research, satellite images of Landsat 8 sensor on 13/03/2020 and Envy 4.5 software for processing satellite images and classification of images, and from GIS 10.3 software for creating educational map, ground reality map and layer format conversion were used. Google Earth Pro 4.2 software was used to collect land points and Excel and Office 2018 software were used to generate data tables. According to the purpose of the study, only from visible bands, near infrared, infrared, short wavelength (with cell size of 30 m) and panchromatic band (with cell size of 15 m), to extract spectral values ​​corresponding to the plot of ground samples and statistical analysis have been used. In this study, in order to control the quality of data and increase awareness of atmospheric, geometric and radiometric errors, the data were first studied (Kiani et al, 2014). In the images prepared for this study, due to the newness Landsat 8 satellite and also the non-mountainous area, no device error was observed. The results of the evaluation of the classification accuracy with four algorithms of maximum likelihood classification, minimum distance from the mean, Mahlon distance and parallel level were shown in Tables 2 and 3. In order to better show the effects and reduce the number of bands in the data and to compact the most information of the main bands in the fewest number of bands, principal component analysis was used and the appropriate band combinations were selected. According to research on Google Earth software data, the reseults showed a high confirmation of the accuracy and precision of the software data (Pakravan et al, 2012). In this research, Google Earth software has been used to determine the accuracy of the mentioned classifications. Then, in order to prepare an educational sample from the image of the main components, the three main bands are combined, after which a number of areas or levels are selected as a sample to be used to classify information (Firoozinejad et al, 2014). Also, after carefully selecting the educational samples, the classification of the classes was done using the Training Sample Manager tool and the study area was examined in the classification with 7 user classes (Gong et al, 1996). According to the sampling, 7 land uses were identified in the study area, which include agricultural land, medium rangeland, poor rangeland, barren land, rock outcrop, plains and canals, and residential areas due to errors in the study area occurred with the wasteland as a class. According to the purpose of the study, the baseline image was classified with four monitored classifications, i.e., maximum probability, minimum distance from the mean, Mahalano distance and parallel surfaces. Then it was applied in order to remove single and scattered pixels on the surface of the classified image and also to obtain the desired image of the 3 * 3 model filter. Then, using the obtained results, the accuracy and precision of the classification and further evaluations were calculated using Envy software.

        Preparing land use maps in the study of surface and basement resources and information on current conditions and planning for sustainable management in the future are among the basic principles. Today, the use of remote sensing data and quantitative statistical methods are very common to prepare land use maps (Arkhi, 2014). Easy-to-reach, access to remote and mountainous areas, low cost of data extraction in a short time, wide coverage and reproducibility are some of the benefits of remote sensing data that has been widely used since the last decade (Sofali and Khodarahmi, 2011). According to Tables 2 and 3, the highest producer and land use accuracy is related to agricultural lands, poor rangeland, medium rangeland, barren lands and rock outcrop related to the maximum probability method, which indicates a high percentage of pixels related to the mentioned land uses. In all methods of land use classification, medium rangeland and barren lands had the highest producer accuracy. Mahalanui classification and minimum distance from the average in all land uses were close to the percentage of producer accuracy and only in agricultural lands there was a big difference, which shows the similarity of the two methods mentioned. Table 4 does not classify the parallel surface classification method for 10.2% of pixels. The reason for not classifying some pixels is that the parallel surface method uses the minimum and maximum pixel values ​​for classification. Therefore, the numerical value of the pixels may not be in the range of minimum and maximum classes and may not be known in the range of classes. Lack of classification of pixels means that this method is not used much in research (Yousefi et al, 2014). According to the results of this study, it can be concluded that the maximum likelihood method with a strong statistical basis distinguishes the boundary between classes better than other classification methods (Ahmad et al, 2013). Due to the high spectral resolution of the OLI sensor, the maximum likelihood method provides the best results for the supervised classification of OLI sensor data and the results of this study are consistent with the results of other researchers (Yousefi et al, 2014; Ahmadpour et al, 2014; Ahmad et al, 2013; Nazari et al 2013; Firouzinejad et al, 2014; Kiani et al, 2014).

     One of the most essential information needed by natural resource managers and decision makers is land use maps. Today, remote sensing technology provides a good opportunity to prepare user maps. The value and efficiency of land use maps depend on their accuracy and precision. The purpose of this study was to investigate the efficiency of supervised classification algorithms in preparing land use maps. For this purpose, Landsat 8 satellite OLI images were taken from Alam Rudasht basin on 12/23/1398 and after geometric, radiometric and atmospheric corrections, principal component analysis was performed and appropriate band compositions were selected. The four monitored classification algorithms of maximum probability, minimum distance from mean, Mahalano distance and parallel levels were compared to prepare land use map. Then, in order to remove single and scattered pixels on the surface of the classified image and also to obtain the desired image, a 3 * 3 model filter was applied. The ground reality map was prepared using satellite images to determine the accuracy of the classification. Results of maximum probability algorithms, minimum distance from mean, Mahalano distance, parallel levels with overall accuracy of 88.32, 72, 76.65, 53.3 and kappa coefficient of 0.87, 0.68, 0.73 and 0.45. Finally, the maximum likelihood method was calculated with an overall accuracy of 88.32 and a kappa coefficient of 0.87. The most accurate method is to prepare a land use map.

      Accumulation of sediments in dam reservoirs is one of the most important problems in the operation and maintenance of reservoir dams and this phenomenon reduces the useful life of dams. Reservoir sediment changes depend on factors such as the amount of sediment, sediment transport rate, sediment type, reservoir performance, reservoir characteristics and river flow. With the entry of sediment and its accumulation in the reservoir of the dam, the effective water storage capacity decreases. This in turn will reduce water storage capacity and loss of reservoir flood capacity. If sediment accumulates near the dam body, it may cause the bottom drains of the intake valves to be buried and can also impede their operation. In addition, sediment can erode the turbines and the lower valves of the reservoirs. The load on the dam body also increases. In this study, the sedimentation situation in the reservoir of Kamal Saleh Dam during the next 50 years will be investigated.

      More than 35 km of the river was considered for simulation. By entering the TIN map into Arc VIEW GIS software, information related to preparing the map for entering into Hec-Ras software was added with the help of Hec-Georas extension. This information included drawing lines related to the coasts, drawing lines related to the flood plain, drawing cross sections and drawing the central line. By entering the information into Hec-Ras software, REPORT output was taken in this software. In this output, the information is obtained as a text file. Therefore, they can be used to enter the text file of GSTARS4 software. In the next step, the required information was entered into GSTARS4 software. This information included 171 cross sections. Also information such as manning roughness, river flow and sediment, sediment model and stage-discharge were considered. To run the model and investigate sedimentation, two modes were considered for examining the water level inside the dam. One mode was the maximum water level in the dam (full dam) and the other was semi-full dam. Due to the water level inside the dam, sedimentation can change in these two cases. Finally, the software was run for 50 years of sedimentation. There are 14 models for sediment transport in GSTARS4 software among which the Acker and the Yang models had a suitable grain size diameter range to be used. The results showed that the use of two models will have similar results and the changes related to erosion, sedimentation and sedimentation front near the reservoir will give similar results for the two models. Therefore, the Yang model was used to continue the work.

       The results of model showed that for full and semi-full dam conditions, the sedimentation location will be different, so that in full sediment dam condition, it starts approximately from 10480 meters distance from dam and continues up to 7236 meters and then stops. But for the dam in the semi-full state, the sedimentation starts from 5766 meters and continues up to 2547 meters distance from dam. Therefore, sedimentation in the full dam reservoir starts about 4714 meters earlier and also ends earlier at about 4690 meters distance from the downstream. The distance that sedimentation is done in the case of full dam is 3243 meters and its maximum depth is 14.46 meters and for semi-full dam is 3220 meters and its maximum depth is 12.79 meters. Investigation of the cross sections showed that at a distance that sedimentation occurred in the case of a full dam (10.5 to 7 km), not only sedimentation did not occur for the semi-full dam, but also erosion is observed in the river. Of course, this erosion was obtained in a small width of the river (approximately 20 meters in width). On the other hand, if the Acker-White model was used to investigate sedimentation in the reservoir, erosion in this area was almost not observed in the case of a semi-full dam. Therefore, erosion in this area can be neglected for the semi-full dam and the cross section can be considered for the semi-full dam with the same initial bed. Also, due to the small width of erosion, it can be prevented by erosion control methods.

       It seems that there is not much difference in the amount of sedimentation in terms of sedimentation distance and its height, and only the sedimentation location has been moved, which in the semi-full dam reservoir, sedimentation is closer to the dam. This shows that the level of water in the dam can have a great impact on sedimentation in its reservoir. Of course, it should be noted that the dam is usually not in full state, so to determine the deposition, two states of full and semi-full dams were considered. Therefore, the deposition front can be placed between these two modes according to the operating condition.

Dams are one of the most important human structures along rivers that are constructed with the aim of generating electricity, flood control, and providing water for agriculture and urban centers. Today, very few large and small rivers remain uncontrolled; what is important geomorphologically is the changes that occur in the performance of erosion processes downstream of the river after dam construction. These changes are not limited to after dam construction, but these morphological changes are the result of changes in the performance of erosion and deposition processes in drainage basins, completely transforming the face of the basin and canals in the area close to the constructed dam. Analyzing the geomorphological changes of rivers due to the creation of human facilities such as dams is one of the most important tasks of geomorphologists. This study is done with different models and methods. Among the common methods of the last decade are the GCD model and machine learning. The GCD method is the result of subtracting two digital elevation models at different times, which are produced by different methods of these dams. To analyze the geomorphological changes caused by the construction of the dams in downstream of the river, in addition to using historical dams, machine learning methods can be used for more accurate modeling by involving a variety of effective maps in detecting changes. The main purpose of this study is to apply the machine learning method using the data obtained from the GCD model to generate regression maps due to the impact of dam operation downstream of the river.

This study was carried out in the Sojasrood River and downstream of Golaber dam. Software and tools used in this research included the following: Arcgis, envi, sagagis software, R software, GCD software and extension, Google Earth Engine system, AutoCAD software, Excel, topographic maps of 1/50000, Elevation digital model and Garmin GPS. Machine learning methods were used to evaluate the effects of Golaber Dam in the period before and after the construction of the dam. In order to access the data required for this research, digital elevation models of stereo pair images of L1A series and L1B satellite ester were used as time series. First, through the GCD model, the volume changes of erosion and sediment downstream of the dam were calculated. Then, the data obtained from this model were used as a target variable along with nine layers of geomorphometry and precipitation and runoff as predictive data to implement machine learning algorithms in three methods of multiple linear regression, decision tree, and random forest. 70% of the data were used for modeling and 30% of the data were used for evaluation in R programming software.

Given that continuous data on erosion and sedimentation rates from the GCD model have been used for the machine learning method, naturally, a regression method (prediction) should be used for the output of the machine learning models. Three steps were taken to achieve the result of machine learning. First, the models were run one by one in R software and evaluated with 30% of the experimental data, and finally, the model maps and their correlation coefficient and RMSE error were calculated. Comparison of the output results of multiple linear regression models and decision tree and random forest showed differences in statistical data and time series maps before and after dam construction. Therefore, the output maps of the models before and after the construction of the dam were also different from each other. The main reason for this is a significant reduction in the runoff, land-use changes, increased vegetation of the bed and riverside, which has led to changes in the independent variable research data in the period after the construction of the dam. Although statistically in multiple linear regression, the p-value was less than 0.05, the output of maps of this model were associated with a large error. And the model did not predict the rate of erosion and sediment well. In the multiple linear regression model, the correlation coefficient of the map before the construction of the dam was higher than the period after the construction of the dam. CART method was used for decision tree modeling. The map produced by this method with a correlation coefficient above 0.6 showed better performance compared to the multiple linear regression model. The best method for modeling erosion and sedimentation rates in both periods was the random forest method. This model with a correlation coefficient above 0.7 provided the most accurate prediction in this study.

Various methods and models have been proposed to estimate the rate of erosion and sediment in rivers. In recent years, new and more accurate models have been formed, especially in the analysis of the time series of river developments. New methods include the GCD model and machine learning (ML). In this study, in order to observe the changes in erosion and sedimentation rate due to the construction of Golaber Dam in the period before and after its construction, first, the GCD model of volumetric erosion and sedimentation changes was estimated through the model of published errors and multiple time series maps were produced. Then, from the data obtained from this model, along with maps and geomorphometric layers and maximum rainfall and runoff data, in order to more accurately predict the impact of the dam on the riverbed in terms of erosion and sedimentation rates and geomorphological changes of the river, machine learning method was used. The results of modeling showed that dam utilization was strongly effective in erosion and sedimentation of river bed and Random Forest algorithm with a correlation coefficient above 70% and RMSE less than the other two models showed the best prediction for both periods before and after dam construction. The maps produced by the decision tree method also modeled the erosion and sedimentation process in the riverbed in both time series analyses well, but the output of the linear regression model was not accurate enough. For an overview of machine learning algorithms, in addition to evaluating the experimental data of the models themselves, the overall average results of some morphometric indices of the river such as number of meanders, center angle, channel length, and Sinuosity index were also evaluated. This comparison showed the accuracy of modeling decision tree and random forest algorithms applied in the present study.

By developing GIS and remote sensing technology, the widespread access possibility and local distribution of hydrological management parameters and variables have become practical. Runoff rainfall modeling is always an important and continuous need for practical issues in the fields of water resources evaluation, flood forecasting, engineering canals designing, and many other goals (Bone, 2001). Calculation of runoff-rainfall results has been made practical and operationalized by using GIS techniques and a distributed hydrological model. The WetSpa runoff-rainfall model is a hydrological-distribution model that was developed in Brussels in 1997 (Wange et al, 1997) and also, had been used in various research and executive projects by developing in various models. This hydrological model has the capability of performing simulations at the pixel level and because of this, it provides the possibility of using aerial and satellite images with accurate information measured at the basin level, which has a local distribution (Hooshyarypor at el, 1397). Due to the high uncertainties of hydrological parameters, the calibration model is one of the most important part of modeling whose optimization techniques are mainly are used for such purposes. There are various methods for optimization, sensitivity analysis, and also evaluation of uncertainty of models. Accordingly, this paper’s purpose is to calibrate the WetSpa hydrological model with a multi-objective optimization approach that uses the Social Spider Algorithm (SSA) and the Black Widow Spider (BWO) techniques. By this point of view, to achieve a reliable prediction, in addition to the usual discharges, the model must be able to predict high and low discharges accurately (including maximum and minimum), so the objective functions are selected in a way that the best match between observational and computational values can be achieved during the Vasanji process.

WetSpa Model: WetSpa is a continuous local and temporal model in which all of the simulations are conducted continuously. The WetSpa model displays the water and energy balance for each calculation cell, considers rainfall processes, vegetation, snowmelt, wetting, infiltration, evapotranspiration, leakage, surface runoff, wall flow, and groundwater flow. The hydrological system simulated by this model consists of four layers: vegetation, soil surface, root zone, and saturated groundwater table.The Optimization algorithms: The Social Spider Optimization (SSO) algorithm is a new optimization approach which is proposed in 2013 by Kause et al. Another optimization approach used in this paper is the Black Widow Spider (BWO) optimization algorithm. During the day, the black widow spider is out of sight and is mostly nocturnal, and rotates its network during the night. Generally, the widow spends most of her adult life on the same site (Andrite and Banta, 2002).Objective functions and model evaluation: In this paper, to evaluate the model, five statistical indices have been used of correlation coefficient (r), Root Mean Square Error (RMSE), mean absolute error (MAE), Nash-Sutcliffe index, and Nash-Sutcliffe logarithmic index. To increase the accuracy of the model in predicting the minimum and maximum flows, two alogarithm Nash-Sutcliffe and Nash-Sutcliffe logarithmic criteria were used.Zashk basin and model data: Zashk Basin is located in Khorasan Razavi province and in the west of Mashhad with an area of 65.56 square kilometers.

In this part of the paper, the calibration results of the WetSpa model using social spider and black widow spider algorithms are presented. The problem decision variables are the 11 global parameters illustrated in Table 1. Objective functions must be selected in a way that at the end of the calibration process the best match between the observed and computational values can be obtained. Some of these functions give more weight in high flows, while others consider more weight in low flows and have more emphasis on them. In this regard, Nash-Sutcliffe (NS) criterion and its logarithmic form (NS-Log) have been used. In this paper, the results obtained based on these two functions are illustrated. After determining all the necessary local networks in basin modeling, precipitation, evaporation, temperature, and discharge information from 2009 to 2011 were used to calibrate the model and 2012 to 2014 data were used to validate the results. Each of the optimization algorithms was conducted with an initial population size of 100 people and over 100 generations (number of times the optimization model was executed). As it was observed, the answers obtained by BWO are better than the answers of SSO due to its higher NS values. According to the obtained results, NS and NS-Log values are generally from -2.3 to 0.75 and from -0.165 to -0.01, respectively. This problem illustrates that the calibrated model has been more successful in simulating low discharges. In fact, since the model has been executed continuously, the simulation results in 2008 are considered as the Warm-Up period.

This paper calibrated the WetSpa distributed rainfall-runoff model. To calibrate the model, two evolutionary optimization algorithms of social spider (SSO) and black widow spider (BWO) were used in the Zashk basin of Mashhad. The results obtained from the usage of the model in this basin illustrated the satisfactory capability of these algorithms in calibrating the WetSpa model. Comparing the results obtained from the SSO and BWO algorithms illustrated that in the multi-objective calibration problem, the BWO algorithm was slightly more successful than the SSO model. Also, the results indicated that the simulation quality of low discharges was higher than the high ones. The reason for this problem, firstly, can be due to the lower abundance of high discharges in the evaluated data set and secondly, the relatively slow response of the model to changes in hydrological conditions in flood conditions in the catchment basin, because the temporary groundwater storage coefficient (K5) allocated a large amount for itself that can directly affect the reduction of surface runoff. On the other hand, the weakness of the results in predicting some low discharges can be related to the optimal value of groundwater recession coefficient (K2), which has taken a small amount in the calibration process. The results of this study illustrated that the effect of the surface runoff coefficient on the model results is much greater than other parameters (about 36%).

At the beginning of the 21st century, the world has witnessed great natural and unnatural disasters. The Great Hurricane Katrina, the Southeast Asian tsunami, the Bam earthquake, and dozens of similar incidents, large and small, repeatedly remind the world that predictions, solutions, and ways to deal with these disasters are still inadequate. And only a small number of nations in the world learn and apply the science and technology of managing and controlling such disasters. In recent years, disaster relief agencies and organizations have focused most of their work on achieving a disaster resilient community. Therefore, risk reduction programs in the crisis management organization should seek to create and strengthen the characteristics of resilient societies and in the accident management chain, the concept of resilience should be considered and promoting resilience and reducing its risks should be increasingly the agenda of planners and politicians. This study was conducted to investigate the effects of resilience erosion factors in the worn-out urban texture of Ilam city to reduce the effects of natural disasters. Accordingly, this study tries to evaluate the relationship between erosion resilience and the risk of natural disasters and to study the indicators and factors affecting physical resilience.

The present research is based on library and field studies in terms of development and has an applied purpose in terms of descriptive-analytical methodology adopted. To achieve the objectives of the research, indicators were extracted in two sections and 8 categories including material, building age, skeleton, number of floors, granulation and permeability in the physical section and slope, soil type in the erosive section. Then the model of erosion and physical resilience through the colonial competition algorithm (MST minimum cover tree) in Matlab 2016 software environment was presented. And for specialization of the studied indicators in the worn-out texture of Ilam city, the method of tracking analysis tool has been used in the network analysis process in ArcGIS software environment.

In order to investigate the effects of erosion and physical factors, resilience was performed through the tree of minimum condition according to the analysis steps and a comparison matrix of 8 * 8 was prepared and its model was presented. Based on the input of real and directional network input information in the worn-out texture of Ilam city for identifying the resilience status, different effects and patterns were presented according to the eight indicators. The output corresponding to the most optimal scenarios implemented in MATLAB 2016 environment was presented in the format. Based on the existing priorities, there are 8 indicators to study the level of worn-out texture in Ilam city to study the status of resilience. By transferring this data and information to the Arc Gis software environment, we spatialized these indicators. Also, the structure of the first scenarios was used in the local search phase and the next two structures were used in the jump phase. In each structure, after determining the locations and assigning the indicators to this physical context, the optimal paths to achieve the goal were updated through central network analysis and in the ArcGis environment for each source/destination node pair. In each scenario answer, if the path between the source/destination node pair included two scenarios, then one of these scenarios would be selected according to the indicators and constraints in the physical context. The results of this study also showed that among the spatial indices, D and H indices, i.e., building age and permeability, obtained the highest score among the analytical codes. In order to study the spatialization of the existing scenarios for the erosive and physical characteristics of the worn texture of Ilam city, first all the information of Matlab software environment was transferred to GIS environment by reading and correcting the information and finally through Tracking Analyst Tools and Make Tracking Layer tool the existing scenarios and codes were constructed. The status of construction and design of the purpose-built network structure for erosion and physical indicators was presented in such a way that at the level of 8 indicators studied in two parts, the following results were obtained: In the erosion index section, one condition was completely suitable, three conditions were suitable, 5 conditions were moderate and 1 condition was completely unsuitable. In the physical index section, there were one completely suitable condition, three suitable conditions, 5 moderate conditions and 1 completely inappropriate condition. In order to determine the current status of resilience indices in the worn-out texture of Ilam city, after classifying the indices in ArcGis software environment, the type, area and percentage of each index were extracted. According to Table 6, after determining the current status of resilience indices in the area of ​​eroded texture of Ilam city, the percentage and area of ​​each index from high resilience to low resilience were presented in four categories for each index. In the material index, the highest percentage, ie 33.61%, was in the cement block type and had a low resilience status. In the building age index, the highest percentage, ie 47.00%, was in the type over 30 years old and had a very low resilience status. In the skeleton index, the highest percentage, ie 33.57%, was in the concrete type and had a high resilience status. In the index of number of floors, the highest percentage, ie 47.11%, was in the type of one class and the resilience status was very low. In the granulation index, the highest percentage, ie 34.13%, was less than 100 meters and the resilience status was very low. In the permeability index, the highest percentage, ie 62.78%, was less than six meters and the resilience was very low. To investigate the final resilience of worn texture in Ilam city, the amount of physical resilience was shown at the level of 6 indicators, so that at this stage, after aggregation of the mentioned indicators in Arc Gis software environment, the final resilience of the building resilience was classified into very low to very high. They are categorized to plan for natural disasters to deal with any potential crises.

In the present study, after extracting the erosion and physical resilience indices in the eroded texture of Ilam city, indices in two parts and 8 categories including material type, building age, skeleton, number of floors, granulation and permeability in the physical part, slope, and soil were extracted in the erosion section. Then, the priorities in resilience levels from very high resilience to very low resilience were identified in seven categories. The results of the research are as follows: Among the spatial indices, D and H indices, i.e., building age and permeability, obtained the highest score among the analytical codes obtained from scenario writing in the content programming environment in such a way that significant parts of the worn texture of Ilam city are in the range of medium to very low resilience. Also, the central part of the city, which corresponds to the dilapidated fabric of the city, due to the lack of a standard structural system and sustainable materials and the inability of residents to create safe areas indicated low and very low resilience. This situation becomes more visible in the event of natural disasters, and the financial and human losses will be doubled. In addition, the results of spatial differences and the study of spatial resilience comparisons showed that 62% of the eroded texture of ​​Ilam city is in the range of relatively low to very low resilience. Percentage and area of ​​each part of the resilience of the worn-out texture of Ilam city were such that 5.25 had a very high resilience interval, 5.65 had a high resilience interval, 8.70 had a relatively high resilience interval, 18.33 had medium resilience interval, 17.90 had a relatively low resilience interval, 21.03 had a low resilience interval, and 21.14 had a very low resilience interval.