فهرست مطالب حمید غلامی

Desertification is a serious environmental and socio-economic threat to the planet. The aim of this study is to use a scientific, reasonable and repeatable method to evaluate the process of vegetation and land use as two important factors in the process of desertification on different scales (local-regional and global). In this study, Sarvestan plain in Fars province was selected as the study area. For this purpose, Landsat images were used for TM (1993), ETM + (2001 and 2006) and OLI / TIRS (2016). Image monitoring was performed using image differentiation, NDVI index difference and land use maps. In 1993, 2001, and 1993, and 2016 difference maps, the decrease in the amount of water in the mouth of Lake Maharloo can be clearly seen as increasing changes in the infrared band. The results of the difference between the vegetation index and the increase in vegetation in the form of agricultural lands in 2016 compared to 2006 and 1993. According to the results of the monitoring classification, from 1993 to 2016, irrigated areas decreased from 7.11 hectares to 0.7575 hectares, on the other hand, the level of saline lands increased from 143.99 hectares to 223.83 hectares and the level of cultivated lands increased. (Agricultural and horticultural) has increased from 113.28 hectares to 14/2014 hectares, which due to the importance of saline lands and land use change indicators in the studies of the desertification assessment process, it can be concluded that the desertification process in the study area is growing.

Sand dunes are one of the most important facies of wind erosion. Our understanding of the complex interactions of sand dunes is often limited by the lack of accurate morphological data. The erosion and sedimentation process is very important and there is currently a lack of field data for executive projects, study plans and validation of erosion and sedimentation models. Images of the study area were taken using a Phantom 4 Pro UAV at an altitude of 60 meters on September 22, 2019. This type of UAV, which is small and light, with its 20-megapixel camera and GPS, can provide high quality images. After separating the dunes, a three-dimensional area in terms of square meters and volume in terms of cubic meters were obtained. And the product of a small amount of bulk density of soil in terms of cubic centimeters and the volume of dunes in terms of cubic meters The weight of the dunes was obtained in terms of tons.In the study, the slope percentage and sediment height were determined based on the windward part and the wind shelter of the dune The highest wind area was slope 10-20% And the maximum sediment is in the slope of 70-100% And in the wind shelter, the maximum area was on a slope of 30-50% And the highest sediment was determined in the slope of 70-100% .

Soil erosion by water and wind is one of the biggest environmental threats worldwide which it has various negative consequences such as soil degradation, mitigation of soil fertility, depletion of nutrients and micro-elements, degradation of soil structure, dust storms, reservoir siltation, and etc. on the natural and anthropogenic ecosystems. Therefore, accurate information of land susceptibility to soil erosion hazard by water and wind, and production of spatial maps of these hazards are necessary for mitigation of their consequences. Therefore, the goal of this research is application of two machine learning models for the spatial modelling of water and wind erosion in the Bakhtegan basin, Fars province.

In order to generate the spatial maps of soil erosion by water and wind, we have used 20 and 16 effective factors on water and wind erosion, respectively. Before the modelling stage, the multivariate adaptive regression spline (MARS) feature selection algorithm and multicollinearity test were used to identify the most important factors controlling water and wind erosion. In next stage, two random forest and support vector learning machine techniques were applied to model soil erosion. Area under curve (AUC) was applied to assess the model performance.

According to results, land use, slope, lithology, roughness, coarse fragment, clay, vegetation cover, aspect, soil bulk density and topographic wetness index were identified as the most important factors controlling water erosion. The most important factors controlling wind erosion are including elevation, land use, wind speed, slope, vegetation cover, soil nitrogen, cation exchange capacity, lithology, aspect, clay and bulk density. The AUC value for wind erosion map generated by random forest model was 99, and for water erosion map produced by support vector machine is 96. Finally, we integrated two maps and results indicate an area of study area about 18% has very high susceptibility to wind erosion and low susceptibility to water erosion.

Due to study area has several lakes consisting of Bakhtegan, Maharlo and Tashk, and also several dams, it is so important to supply water for different purposes such as drinking, agricultural, industry and environment. In case of not paying attention to this challenge especially wind erosion in the study area, this case study especially dried beds for lakes can play as sources for generating dust particles. Therefore, the findings of this research can use by managers to mitigate negative effects of this phenomena.

Population growth and the excessive use of natural resources have caused significant changes in natural ecosystems, including a decrease in rainfall and an increase in temperature. The potential exists for them to decrease vegetation and increase barren areas. Serious economic, social, and environmental damage can occur in natural ecosystems due to the destruction of land cover and other damages, such as dust storms. Therefore, ecosystem changes are taking place worldwide, both at the temporal and spatial scale, due to human activities and natural factors. So, investigating the amount of land use/cover changes, their effect on dust storms, and predicting these changes for the coming years can be an important step in reducing and controlling unprincipled changes, planning, and optimizing resource. Climate change and human activities, such as drought, human activities, and non-compliance with water rights, have a significant impact on the Hamon wetland area, so that the dry bed of the wetland has become the main sources of dust. This research is focused on investigating the impact of land use changes on dust storms and forecasting land use changes in the Sistan region for the next 20 years.

The impact of land use changes on dust storms in the Sistan region was examined using Markov chain forecasting methods. For this purpose, first of all, the land use maps of 2002, 2011 and 2022 were prepared using satellite images. An anomalous method was used to investigate climatic parameters, including temperature, rainfall, and the number of days with dust, in the next step. To evaluate climatic changes, it is necessary to use a method that shows long-term changes. The anomaly method was employed for this purpose. The values of this index can be either positiveor negative. In order to predict land use changes for the next 20 years, the combination of the maps of 2002 and 2022 for severe drought conditions were used by using Markov chain and Cell models. The Markov model was predicted to generate multiple images. The transfer probability matrix allows for the expression of the probability that any type of land cover will be found in any location in the future. Despite the accuracy of transmission probabilities for each user is unknown, due to the lack of information on the spatial distribution of users, the Markov model does not have any spatial dependence information.  In contrast, to the automatic network, it is an agent that has the ability to change its state based on the application of the law that shows the new state in accordance with the previous state and the state of its neighbors.

This study examined the impact of land use change on dust in the Sistan region. At first, climatic changes of temperature, rainfall and number of dusty days were investigated and the results showed that the temperature has increased and rainfall has decreased in the Sistan region during the last two decades. The land use maps also showed that in the years when the Hamon wetland has been drained, pastures and dense vegetation have increased and barren lands and salt marshes have decreased. But due to the recent droughts like the year 2022, when a drought has occurred in the region, the use of vegetation and pasture has decreased and barren and salt marshes have increased. These conditions cause an increase in the level of dust in the region. The land use map for severe drought conditions in the next 20 years was predicted using the Markov model.  It showed that in the future, pastures and dense vegetation will decrease, but barren lands and salt marsh areas will increase dramatically. As desertification and wind erosion increase, dust storms will also increase as a result of these conditions. The economic, social, environmental, and health conditions of residents in the region are adversely affected by dust storms. Therefore, proper planning and management can reduce the damages caused by dust storms in the Sistan region.

Sustainable land management is of great importance as one of the components of global sustainable development. In order to achieve sustainable management, planning is necessary, because accurate assessment of the land and reducing the destructive effects of the soil provide a safe environment for production. Knowing the changes and evolutions of land use over a period of time is very important to show the change process over time. . Considering the trend of droughts and destructions in Darab city, the aim of this research is land use based on knowing the capacity and allocating it to the best and most profitable type of use.

In this research, changes in solutes, well ring data and irrigation methods were investigated. Sampling was done from 36 points. In this research, the type of irrigation including four types of drip, strip, rain and strip irrigation was identified and based on that seven factors pH, EC, OC, TNV, N, P2O5, K2O were measured. In the next step, the depth of the well and the volume of the annual water harvest were determined, and based on that, the changes in soil solutes were investigated. Pearson correlation tests were used to check the relationship between variables.

In rain irrigation, pH and EC were standard, TNV was higher than standard, OC, P2O5 , K2O and N were lower than standard. In drip irrigation, the pH in one sample was higher than 8 and one sample was lower than 7, the EC value was higher than the standard in two cases and the TNV value was lower than the standard in 5 samples. OC, N and K2O were below standard in all cases, P2O5 was standard in 5 cases. In strip irrigation, soil pH was between 7 and 8 in all cases, EC was standard in 7 cases, T.N.V was lower than standard in 7 cases. The amount of soil O.C, N, K2O and P2O5 was lower than the standard value in all cases. In strip drip irrigation, the pH of the soil was lower than the standard in one case. The value of EC was lower than the standard value in one case, the value of TNV, OC, N and K2O was lower than the standard in all samples, and the value of P2O5 was standard in one case. The correlation coefficient between the annual water harvesting volume and soil changes showed that, in the tested lands, the relationship between the water harvesting volume with O.C, pH, EC, S.P, T.N.V was negative and the correlation with P2O5, K2O, N was positive. The correlation coefficient between the depth of the well and the fluctuation of soil changes showed that the relationship between the volume of water withdrawal with O.C, pH, EC, SP, TNV was negative and the correlation with N, P2O5, K2O was positive.

Accelerated soil erosion by water is an environmental threat on different continents. Suspended sediment loads in riverine systems resulting from the accelerated erosion due to human activities are a serious threat to the sustainable management of watersheds and ecosystem services therein worldwide. Identifying sediment provenance in the catchments is essential to to mitigate its negative effects consisting of on-site (e.g., decreasing soil depth and depletion of soil organic, degradation of soil structure, and etc.) and off-site effects and to help remedy problems such as eutrophication, and siltation of reservoirs. Among direct and indirect methods used to study the sediment source, sediment fingerprinting is a useful technique for determining contribution of sediment sources within a catchment like agricultural lands, rangelands, barelands, and etc. The successful application of this method reported in fluvial and aeolian environments. In this study, sediment fingerprinting method used to identify sediment sources and quantifying contribution of its sources in the Farghan Catchment in Hormozgan Province.

In this research, 38 surficial samples (0-5 cm) were collected randomly-systematic with a good distribution from the potential sources (consisting of eight samples in agricultural lands, 18 samples from gully erosion sites and 12 samples from barelands and rangelands) and six samples from sediment deposited in the bed of the river in vicinity of catchment outlet, respectively, and after samples preparation, the concentration of the geochemical elements (consisting of major elements, rare earth elements and trace elements) were measured by ICP-OES in the central laboratory of University of Hormozgan. Stepwise discriminant function (DFA) was applied to discriminate the sediment sources, and five tracers consisting of Te, Zr, Ta, Be and Na were selected as the final tracers. Finally, the relative contribution from each source was determined by mixing model.

Based on the results, the mean contribution for the agricultural lands, barelands and rangelands, and gully erosion sited were estimated 16.7, 50.6, and 32.7 %, respectively. Based on the results, a combination of Te, Zr, Ta, Be and Na were able to correctly classify 89.3% of the source sediment samples consisting of agricultural lands, gully erosion sites, barelands and rangelands. Due to high sediment rate, gully erosion sites are one of the important forms of soil erosion by water. The central parts of catchment are the most susceptible region to gully erosion because these areas are covered by lithological formations such as Bangestan, Aghajari and Mishan. Mishan lithological formation is involving the marl, limestone, and the Aghajari outcrop consists of sandstone and marl. The lands of flat plains are covered by quaternary fluvial depositions resulting from the erosion of Aghajari, Mishan and older lithological formations. Due to low slop of central parts of study area, existing young soils and without developed horizons and mismanagement of land uses, the land susceptibility to gully erosion is high in central parts.

Sediment source fingerprinting is a useful technique to investigate the origin of sediment in both windy and fluvial sedimentary environments. The estimated source proportions can help watershed engineers plan the targeting of conservation programmes for soil and water resources and due to the variability of geological units from one region to another, the type of land use management, and the type of soil units of each region, the selected trackers for each region are different, and for this reason, until now researchers are able to provide a comprehensive guide for choosing a tracker. were not optimal in all regions, and this issue is one of the main challenges of sediment fingerprinting.

Drought is a complicated natural phenomenon that occurs basically due to the lack of precipitation over a time period. And its occurrence usually results in great costs on various parts of the natural and the society. Among the various indices in climate drought monitoring, the SPI index (McKee et al., 1993) is the most well-known index, in terms of easy access to its data (rainfall); It is also possible to calculate it in any time window; Ability to calculate magnitude, frequency and continuity; The possibility of quick detection of soil moisture and the possibility of showing the spatial distribution of drought-dominated areas are widely used all over the world (Mishra and Desai, 2005). Standardized Precipitation Index (SPI) can be calculated for any location based on long-term recorded precipitation data. Calculating the SPI drought index in each of the time scales can be considered as one of the advantages of this index. The length of the rainfall data recording period as well as the nature of the probability distributions play an important role in calculating the SPI drought index and these factors are among its limitations.

Methods and materials:

In the present research, the uncertainty of SPI estimation in choosing the length of the statistical period and time scale was investigated using monthly rainfall based on the gamma distribution function at Bandar Abbas synoptic station in two periods of 31 and 64 years. Therefore, the rainfall for the time scales of 3, 6, 12, 24 and 48 months in the two mentioned time periods was calculated and then using the bootstrap method for each rainfall event in each time scale, 1000 random data were generated and the SPI confidence interval was about 95% confidence was calculated. The size of the confidence interval (the difference between the upper band and the lower band) was considered as uncertainty and the absolute ratio error (ARE) between the estimated and observed values was calculated.

ARE and uncertainty due to bootstrap estimation were estimated in two time periods and scales from 3 to 48. The greater the difference between the bands, the lower the certainty and the greater the uncertainty (Vergeni et al., 2015). Therefore, a longer period of time (large number of samplings) has less uncertainty, since the number of samples decreases as the time scale increases, so the uncertainty bandwidth increases. For example, in the 3 and 6-month scale in the 31-year period, the estimation error is 0.09, 0.11 and in the 66-year period is 0.03, 0.05, and the uncertainty has decreased from 0.051, 0.63 and 0.38, 0.41, respectively. In the 12-month scale, the error rate in the short and long term is 0.12 and 0.01, respectively, and the uncertainty is 0.81 and 0.55, respectively. In the 24-month scale, the error in the 31-year period is 0.31, which decreases to 0.1 in the long-term period of 66 years, and in 48 months, it decreases from 0.45 to 0.27, and in the same way, the uncertainty level in the long-term period is Scales 24 and 48 are reduced.Based on the results of two short (31 years) and long (66 years) samples, it showed that there was more uncertainty and error in the samples with a small number, and considering the overestimation or underestimation caused by the length of the short period in the diagnosis of drought classes Historical events have been misjudged and lead to inappropriate drought mitigation measures, as classifying a high-grade drought event (Class 9) to a lower grade (Class 8 or lower) can lead to misleading decision-making.

SPI was able to successfully detect historical droughts in the two investigated periods. The increase of records from 31 to 66 years led to the emergence of one of the most unique features of SPI, i.e. repeatability, reversibility and predictability. The confidence interval resulting from the difference between the upper and lower bands estimated by the bootstrap resampling method indicates uncertainty, the smaller the bandwidth or the result of the difference, the lower the uncertainty and the more reliable it is. In this research, the error caused by bootstrap estimation in 1000 sampling times was also estimated. Based on the obtained results, increasing the number of samples from 31 years to 66 years causes a decrease in uncertainty and error, and as a result, increasing the time scale due to the decrease in the number of samples due to data averaging causes an increase in uncertainty and estimation error. The studied area has It has had long dry and wetter periods in the past and has experienced numerous historical events. Therefore, it is necessary to pay attention to the possible judgments caused by wrong estimation and uncertainty in the observational data, and in this way, seek to reduce the possible damages caused by overestimating or underestimating drought.

In analyzing the frequency of drought as a multifaceted phenomenon, summarizing the event in one variable reduces the reliability of the results. Bivariate and multivariate drought frequency analysis using copula functions makes it possible to estimate the return period of a drought event with specific intensity, duration and peak. In the present study, the stream drought index (SRI-12) was used to investigate the characteristics of drought using the monthly stream of Barnetin Station of the Esteghlal Minab Dam watershed. Spearman's correlation coefficient was used to analyze the dependence between hydrological drought variables and six marginal distribution functions for their modeling. Then, by using Archimedean and elliptical family copula, the best joint was selected and conditional multivariate analysis of hydrological drought was done. The results showed that gamma, lognormal, and Weibull distributions are the best marginal distribution functions for drought intensity, duration, and peak, respectively. The results of the copula fit show that the best copula function in analyzing the dependence between variables is Frank's copula. The three-variable conditional probability of drought severity in 20, 50, 80, and 100-year continuities and peaks of 1 and 1.5 were investigated. These thresholds were chosen considering that they were the most frequent in the study area. The results showed that the probability of drought intensity decreases with the drought duration remaining constant and the peak increase from 1 to 1.5, but with the drought duration and intensity increasing, the drought peak also increases.

The abundance of agricultural waste and fishing is one of the environmental issues in various regions. Converting these wastes to biochar and using them in the soil is a way to enhance diverse ecosystems and plays a vital role in environmental sustainability. In the present study, the effect of shrimp (Fenneropenaeus merguiensis) and eggplant (Solanum melongena) biochar suspension on certain chemical characteristics of sandy soil was investigated. For this purpose, samples of sandy soil from the agricultural lands around Bandar Abbas were collected intact in PVC columns with a height of 25, and diameter of 20 cm. Then the prepared suspension of shrimp and eggplant biochar was added to the soil in a completely random design in three concentrations of 0, 4, and 8 g/l in three replications. After adding the different soil treatments into columns, they were incubated for 100 days at a humidity range of field capacity and 50% of it was kept in nursery condition. Next, some soil chemical properties including of pH, EC, N, K, Ca, Mg, and Na were measured. Statistical analysis and comparison of means at P<0.05 between various treatments were performed using a one-way analysis of variance and the Duncan’s test in SPSS16. The results of ANOVA showed that the application of both types of biochar had a significant effect on the chemical properties of sandy soil, so that the application of shrimp and eggplant waste biochar caused a significant increase in the amount of K, N, and pH, and a decrease in EC, Na, and Ca compared to the control treatment. The maximum value of K was related to the concentration of 8 g/l of shrimp and eggplant biochar and the maximum value of N was obtained by applying 8 g/l of shrimp biochar. In addition, the increase in the concentration of the two types of biochar indicates an increase in K and N values. The lowest CE value was obtained by applying 8 g/l of eggplant biochar. Based on the results obtained, shrimp and eggplant biochar waste are suggested in the sand soil amendment.

Land degradation due to its impact on agricultural production, environment, security and quality of life in the 21st century has been raised as a global issue. It provides evidence such as biodiversity loss and climate pollution. The purpose of this study is to investigate the effects of socio-human factors on land degradation in the coasts of Bushehr, Hormozgan and Sistan and Baluchestan provinces. For this purpose, during the period 1988-2019, Landsat satellite data including OLI, ETM+ and TM sensors and population census information were used. To prepare the land use map, first geometric and radiometric corrections were made. Then, the supervised classification method and the maximum probability algorithm were used. In order to determine the role of effective variables, multivariate regression analysis method was used. SAVI and SI1 were considered as dependent variables, use of man-made areas and population density were also considered as independent variables. Based on the results of the change detection, the land use class of man-made areas and the population variable are associated with an increase of 2.7 and 2.3 times, respectively. Population density in the study period has increased from 96 to 194 people per square kilometer. According to the results of regression analysis, the use of man-made areas was selected as a more influential variable on the SAVI and SI1 indices. On the other hand, indicates the impact of socio-human factors on the process of land degradation by 37%, that the socio-human variables are an important factor and part of the process of land degradation. Therefore, the findings of this study will enable the adoption of fundamental decisions in the formulation of management policies for planners and managers in the field of natural resources and the environment for the sustainability and evaluation of coastal areas.

The phenomenon of desertification occurs in a wide range of climates from arid and semi-arid to semi-humid regions and is caused by natural factors and improper human performance. The Yazdanabad watershed is an active center of desertification in Kerman province, Iran and has been subject to human activities and climatic changes, especially drought.The present research aims to study the desertification potential of the Yazdanabad watershed in Zarand, Kerman province using remote sensing data for a 20-year period from 2000 to 2020.Land use maps were first developed for 2000 and 2020. Then, the relationship between the soil-adjusted vegetation index (SAVI) and the Albedo climatic index derived from TM and OLI Landsat images was used.Finally, using this relationship, the desertification divided index (DDI) was employed to derive different desertification classes.The results show that over 88% of the Yazdanabad watershed has been composed of bare land, which has shrunk over the studied periodwhereas saline land use, agricultural land use, and residential and man-made areas have expanded by about 94.39, 830.91, and 1106.30 ha, respectively.The mean annual trend of both SAVI and Albedo revealed the descending trend of the former and the ascending trend of the latter.The DDI-derived desertification classes in 2000 and 2020 show that over 88% of this plain is in the very severe and severe desertification classes. This means that the plain is extremely vulnerable to land changes and degradation given its climatic conditions and fragile vegetation cover.

 Considering the fact that evaporation affects the planning and operations of water resources as a key process in the hydrologic cycle, predicting its magnitude and patterns, particularly in arid, semi-arid, and hyper-arid environments such as Sistan plain (in northern Sistan-Baluchistan Province, Iran) is of great importance. On the other hand, as accurate estimation of Pan evaporation is regarded as one of the main aspects of water management in such regions, it is crucially important to accurately simulate the pan evaporation based on the available regional meteorological parameters. Therefore, this study sought to investigate the capabilities of soft computing techniques for estimating monthly evaporation in Sistan plain. The results of the study could be helpful for the management of water resources in the Sistan area, allowing the policymakers to develop future projects of water resource management/development plans for the region based on Evaporation estimations.

 various meteorological parameters, including maximum, minimum, and average temperature rates, relative humidity, wind speed, and precipitation rate were used to predict monthly Evaporation using the consistent and uninterrupted historical time series data (1994–2021) collected from three meteorological stations (Zabol, Zahak, and Chahnimeh).
The main purpose of this study was to assess the performance of a soft computing model in simulate pan evaporation. To this end, nine soft computing models, including Model Tree (MT), Random Forest (RF), Support Vector Machines (SVM), Bayesian Ridge Regression (BRR), Gaussian Process (GP), Extreme Gradient Boosting (XGB), Artificial Neural Network (ANN), and Multivariate Adaptive Regression Splines (MARS) were used to predict evaporation at the meteorological stations selected for this research.
On the other hand, the model’s performance was assessed using statistical measures, including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and Taylor diagram. Moreover, to construct predictive models, the dataset was divided into training (70%) and validation (30%) data. Then, eight combinations of input parameters were selected for Zabol and Zahak sites based on the Pearson correlation coefficient between the individual input parameters and evaporation. Finally, the best input combination and the optimal values for different models were determined using the R programming language.

 The different input combinations were determined for the two sites independently based on the inclusion of the weather parameters with the highest coefficient Pearson with evaporation. Then, each model was run using various fixed sets of parameters. For the Zabol station, the minimum temperature rate indicated the greatest correlation coefficient with (0.96), followed by average temperature (0.95) and smallest by rainfall (-0.42). It was also found that the outputs of Zabol and Zahak stations were very close and similar to each other, which could partly be attributed to the proximity of the two stations and their same topography and climatic conditions in the Sistan plain.
On the other hand, the results of assessing the models’ performance indicated that in the validation stage, MT (whose R2 = 0.97 and RMSE=57.3) delivered the best performance in Zabol station under Scenario 1, RF (with its R2 and RMSE being 0.98 61.7, respectively) performed the best under Scenario 2, MT with its R2 and RMSE being 0.97 and 61.27, respectively, showed the best performance under Scenario 3, the ANN and MT (whose R2 and RMSE reported as being 0.96 and 59.9, respectively) put in the best performance under Scenario 4 , and RF with R2 = 0.97 and RMSE=59, 59.24, 58.23, and 58.3 delivered the best performance under scenarios 5 to 8, respectively.
Moreover, the results suggested that adding the number of input variables to the models made no difference in their accuracy level. It was also found that out of eight scenarios investigated in this study, the RF model delivered the performance under five scenarios (2, 5, 6, 7, and 8), and that the MT performed well under two scenarios (1 and 3). Therefore, RF and MT could be introduced as the best soft computing models for simulating and estimating the pan evaporation of the Sistan plain.

 The results showed that with increasing the input of variables to the model, there was not much difference in the accuracy of the models. For example, R2 of scenario 1 with only the minimum monthly temperature input is equal to scenario 8 with eight inputs equal to 0.98 in the validation stage. Therefore, the findings showed that the model with only the minimum monthly temperature input has the same performance as the model with eight inputs The main contribution of this study was introducing a soft computing model to accurately estimate the pan evaporation of the Sistan plain using a meteorological parameter (minimum monthly temperature).

Hendijan wind erosion center is located in Khuzestan Province and southwest of Iran. In the last decade, with the increase of erosion rate and concentration of important dust centers in this area, the study of heavy metal concentration and pollution in this area has become very important. For this purpose, 33 soil samples of this zone were collected based on land use change and with the aim of uniform distribution and analyzed by ICP-MSS. The obtained data show that the highest average concentrations of metals belong to Cd <As <Cu <Pb <Zn <Ni <Cr, respectively. Enrichment indices, geo-accumulation index and contaminant factor showed that the highest pollution in Hindijan area belongs to nickel, chromium, arsenic, cadmium and plumbum, respectively. On the other hand, ecological risk assessment in the mentioned area has shown that this region has a low ecological risk that among the studied metals, the highest ecological risk belongs to the two metals arsenic and cadmium. The results of PCA test showed that the metals nickel, zinc, copper and lead have both non-anthropogenic and anthropogenic sources and the source of arsenic and cadmium metals is human activities. According to the pattern of metal distribution, it can be stated that the activities related to Bahrkan fishing pier on the one hand and oil rigs off the coast of Hindijan oil field on the other hand have caused the concentration of nickel, lead, zinc and copper in the south of the study area. Agricultural activities have also controlled the concentration of cadmium and arsenic metals in this area, and the source of chromium concentration was determined as agricultural effluents, traffic pollution and residual pollution from the 8-year Iraq-Iran war.

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

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

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

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

Increasing industrial activity, followed by the entry of heavy metal pollutants into the aquatic ecosystems, are one of the biggest environmental challenges. Plants are good indicator of metals concentration in water and sediment of aquatic ecosystems. The aim of this study was to investigate the concentration of heavy metals in sediments, roots, stems and leaves of mangrove trees in Khorkhoran protected area as one of the most important mangrove habitats in the Persian Gulf. For this purpose, 9 samples of sediment, leaves, roots and stems were collected from this area and analyzed by ICP-OES to determine the concentration of heavy metals. Shapiro, Kruskal-Wallis, Tukey and bioaccumulation index and transfer coefficient were used for statistical analysis. The results showed that the highest concentrations of heavy metals in sediment samples were specific to nickel, zinc, copper and lead, respectively, and in the plant the highest concentrations were related to zinc, nickel, copper and lead. In general, the process of accumulation of metals in sediment and plant tissues indicates an increase in the concentration of metals in plant tissue with an increase in sediment. Bioaccumulation index showed that the highest and lowest concentrations of heavy metals were in roots and leaves, respectively, and mangrove root is a suitable indicator for measuring mangrove contaminants. The transfer coefficient of nickel metal in the stem and copper metal in the leaf showed the ability of these organs for phytoremediation of these two metals. The results showed mangroves are repelling species for other metals.

The Ferula spp. is one of the most important genus of rangeland plants in the northeastern region of Iran and plays an important role in soil conservation and livelihood of rangeland stockholder. However, improper exploitation and destruction of the plant's habitats are among the factors threatening this natural capital. This study was conducted to identify potential habitats of this genus in the two provinces of Khorasan Razavi and North Khorasan, which can be a step to facilitate and develop replanting and rehabilitation operations of this genus. Spatial data of the presence of this genus with a pixel size of 1100*1100 m were used as actual suitable sites for its growth and layers of environmental information such as climate, soil, geology and physiography were used as predictor variables. First, in Biomapper software version 4, the correlation of prediction variables was examined and important independent variables were selected. The potential habitat map extracted from distribution model mapped using Maxent 3.3 software. The results showed that the model used had good accuracy and reached the AUC of 0.94. In terms of importance. Investigating the input variables to the model using the Jackknife test showed that the variables of the slope, Geology, Soilland, habitat altitude (dem) and the annual average temperature (Bio1) have most important roles in the potential habitat of Ferula spp. In the study area the results of this study can be used to identify the ecological needs of species under study and development and revitalization of its habitats.

Astracantha gossypina (Fisch.) is one of the most important rangeland plants in the northeastern region of Iran and has a great role in soil conservation and the economy of ranchers. unreasonable uses and management of desire plant species usually led to species loss and the replacement of endemic and specialist species by invasive endemic or exotic species. This study was conducted to determine the potential habitat of As. gossypina species in the rangelands of Khorasan Razavi and North Khorasan provinces using MaxEnt model. In this purpose, 757 species presence points in 17 different areas recorded by GPSMap 60CSx in field sampling. Environmental variables including 19 bioclimatic layers, 3 layers of slope, direction and altitude, geology and soil data layers (texture, soil potential, hydrological groups) as predictor variables analyzed for correlation and corelated variables were removed. Analysis of prediction layers and presence points performed using Maxent 3.3 software. According to the results the area under the curve index (AUC = 0.98) showed, the maximum entropy model had good accuracy and efficiency in detecting the factors affecting the geographical distribution of the species. Based on the jackknife test results, environmental factors included DEM, SOILLAND, BIO1, geology, BIO17, BIO19 and BIO15 respectively had the most role in determining the habitat suitability of the species. Finally, the study area was divided into four suitability classes and more than 218 thousand hectares, equivalent to 1.52% had moderate to good potential for the growth and harvesting of the species.

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

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

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

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

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

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

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

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

Ferula assa-foiteda L., as one of the most important range plants in northeastern Iran, plays an important role in soil conservation and the economy of rangeland stockholder. However, misuse and destruction of its habitats are among the factors threatening this natural capital. This study was conducted to identify potential habitats of this species in the two provinces of Khorasan Razavi and North Khorasan, which can be a step towards to facilitate and develop replanting and rehabilitation operations of this species. Spatial data of the presence of this species with a pixel size of 1100 × 1100 m were used as actual suitable places for its growth and layers of environmental information such as climate, soil, geology and physiography were used as predictor variables. First, in Biomaper Version 4, the correlation of prediction variables was examined and independent variables were selected. The potential habitat areas extracted from the species distribution model mapped with the Maxent 3.3 software. The results showed that the used model had good accuracy and reached to AUC of 0.97. In terms of significance, the study of input variables to the model using the Jackknife test showed that, respectively, land unit components, seasonal temperature, geological formation, dominant land slope, habitat height and daily average temperature are suitable for the potential habitat of Ferula assa-foiteda L. species in the level of the study area is of the utmost importance. The results of this study can be used to identify the ecological needs of the species (Ferula assa-foiteda L.) and to develop and rehabilitate their habitats.

Land degradation is a multifaceted phenomenon, which is caused by various variables, including climate, land use changes and socio-human activities. In order to investigate the effects of climatic parameters on land degradation in five second degree watersheds (South Baluchistan, Bandar Abbas - Sedij, Kal - Mehran, Hillah and Mond) located in the entire Persian Gulf and Oman Sea Watershed, observational data E32 of synoptic stations were used in the mentioned catchment areas for of 31- year period (1988-2019). The IDW algorithm was used to map the climatic parameters. The results of the change detection showed that the trend of temperature class changes of 25– 27.5 follows an increasing rate of 19.03%, and the precipitation class is less than 150 mm in the region. The region is also facing an increasing trend of 17.3%. The trend of the evaporation parameter is such that the 2500-2750 and 300-3250 mm classes with the changes of -5.4, 8.3 percent, respectively, have the most decreasing and increasing effects. Moreover, the wind speed classes of less than 2 and 3-4 meters per second with changes of 5.7 and -7.5 percent show the highest increase and decrease respectively, based on the findings of the regression model, there is a significant relationship at the 0.05% level between the climatic variables (precipitation, temperature, evaporation and wind speed) on one hands and the vegetation index and salinity and the precipitation parameter on the others show the greatest effect. Considering that the four mentioned climatic variables explain 47.6% and 40.5% of the changes in the dependent variable of vegetation index and salinity, respectively, it can be concluded that part of the changes in vegetation and salinity are due to the conditions. As the climate prevails in the region, the poor vegetation and salinity were constantly fluctuating during the study period; consequently, the process of degradation followed an increasing and decreasing rate. Therefore, being aware of the effects of climatic parameters on the fluctuation of vegetation and salinity indices in a long period of 31 years, it is possible to make the necessary predictions for the optimal management of natural resources, especially during droughts. This enables the concerned authorities to control the development stages of land degradation in the coastal catchment areas of ​​the Persian Gulf and the Sea of ​​Oman.

Desertification, a manifest sign of land degradation is mostly due to unreasonable treatment, and in many cases, over-exploitation of natural resources, mainly soil, and water. Therefore, the purpose of this study was to identify and introduce the degree of impact of human activities from the perspective of water and land management in the desertification of the Sarvestan Sub-basin. Quantitative and qualitative changes in groundwater, detection of changes in farm field expanses, and the current state of water criteria (indicators: groundwater loss, EC, irrigation systems, SAR), and agricultural criteria (indicators: yield, cultivation pattern, use of inputs and machinery) using the Iranian model, the IMDPA. The water resources of the Sarvestan Sub-basin have decreased in quality (an increase of 2700 µS/m of salinity during the preceding 21 years) and quantity (subsidence of 10 meters of water level in 25 years). The cultivated area has expanded 196% from 1993 to 2016. An application of the IMDPA model indicates that the electrical conductivity index, with a numerical value of 3.4, had the highest effect (extreme condition), and application index of agricultural inputs and machinery with a numerical value of 1.7 had the lowest effect (moderate condition) in the Sarvestan Sub-basin. The water quantity with a numerical value of 2.56 was the dominant criterion, and along with the agriculture criterion, was placed in the moderate class.

Gully erosion is one of the causes of land degradation, which causes a lot of damage with the loss of fertile soil. For this purpose, in the present study, using the Cochran relation, 40 gullies were selected for measurement in the Mazayjan watershed located in the southeast of Fars province. Factor analysis method was used to determine the factors affecting the classification of samples. Backward multivariate regression was used to determine the most effective factors in the formation, expansion and production of gutter sediment. The results showed that the creation area of ​​the gullies is a function of the variables of bare soil, organic matter and sand. So that with each percentage increase of bare soil, the amount of 0.379 is added to the creation area of ​​the watershed and at the rate of each unit, the percentage of soil organic matter and the percentage of sand are 0.349 and 0.363 units of the creation area are reduced respectively. Also, in determining the factors affecting the volume of gullies, the percentage of sand was the most important factor was reducing the volume of gullies. Therefore, in order to prevent the development of gullies and reduce the production of sediment, more attention should be paid to erosion control in the headcutchr('39')s gullies. It helped reduce gully erosion by increasing roughness and reducing runoff.

Soil salinity is one of the factors limiting plant growth and land degradation. The purpose of this study is to apply and access to soil salinity database which are inaccessible in arid and semi-arid regions using laboratory methods and GIS techniques. In this research, an attempt has been made to identify the soil salinity trend and its minerals by digging 8 profiles with an average depth of 1.5 meters and sampling water from 4 points on a transect from the Kenic line to the Playa in Hoz-e Sultan Qom. Soil water samples were taken from each soil profile at the end of the growing season in late Shahrivar and analyzed in the laboratory. The results showed that the trend of salinity changes from Kenic line to Playa is such that chlorides, sulfates, carbonates and finally calcium carbonates are observed, respectively. This trend was significant in Hoz-e-Soltan region. In this study, soil PH range from 6.5 to 8.9, salinity starts from 1.5 ds / m / m2 near the Conic line and increases to 2.30 ds / m2 next to the salt pan. Also, in the study area, sodium (soda) soils, the percentage of exchangeable sodium is more than 15 and the ratio of sodium absorption in the saturated extract is more than 13