hadi memarian

Gully erosion, a significant environmental issue, can lead to severe consequences like soil loss, habitat destruction, and water pollution. To mitigate its impact, accurate mapping of land sensitivity to gully erosion is crucial. Machine learning models offer a powerful approach to predict and map gully erosion susceptibility. This study focuses on the Mukhtaran basin in South Khorasan province, Iran. By employing various machine learning techniques, including GLM, GBM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt, the researchers aimed to identify the most suitable model for predicting gully erosion. Twenty-two environmental factors were selected and analyzed, with a focus on physiographic, climatic, hydrological, soil, land surface/cover, and geological variables. The results showed that the random forest (RF) and ensemble (ESMs) models demonstrated the highest accuracy in predicting gully erosion susceptibility, with a TSS index of 0.97. Sensitivity analysis revealed that the digital elevation model, soil electrical conductivity, bare soil percentage, land unit components, geology, runoff coefficient, and maximum storage capacity were the most influential factors. The study emphasizes the potential of machine learning models in generating accurate gully erosion susceptibility maps. However, further research is needed to explore additional factors and improve data quality. By combining topographic/hydrologic indices with machine learning models, more precise estimates of gully paths can be obtained for use in process-based models.

Soil erosion, a critical environmental issue, endangers ecosystems, agriculture, and food security. This study aims to develop a precise and comprehensive method for assessing soil vulnerability to erosion by exploring various advanced statistical and machine learning models. Combining spatial modeling and machine learning, this research seeks to create a model capable of accurately simulating complex soil erosion processes and identifying erosion-prone areas with high precision. Our findings reveal that machine learning models like Random Forest and Artificial Neural Networks excel at predicting soil vulnerability. This breakthrough can significantly aid in management planning to mitigate soil erosion and conserve natural resources.  To comprehensively assess wind erosion in the study area, a multi-stage approach was employed. Initially, a robust foundation for subsequent analyses was established by gathering diverse data, including high-resolution topographic maps, vegetation cover, and soil and geology data, all integrated using GIS. Subsequently, high-quality aerial imagery and extensive field visits were utilized to precisely locate wind erosion points. This step was crucial for ensuring the accuracy and reliability of modeling input data. During field visits, a GPS device was used to record the exact locations of various wind erosion types, such as ripples, dunes of varying density, ridges with different grain sizes and densities, surface erosion in the form of ridges, signs of erosion and transport, and sand plains. Following data collection, we proceeded to identify and select environmental variables that significantly influence wind erosion. From the 25 identified environmental variables, suitable variables for modeling were selected using statistical analysis and the Variance Inflation Factor (VIF) criterion, ensuring a value less than 10. This step aimed to reduce multicollinearity among variables and enhance model accuracy. To model the spatial distribution of wind erosion, a wide range of machine learning algorithms were employed. These algorithms included GLM, GBM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt, along with a powerful ensemble model (ESMs) developed in R software. The selection of these algorithms was based on the complex nature of wind erosion and the superior ability of these algorithms to model nonlinear relationships. The data was divided into training and validation sets with a 70:30 ratio and repeated five times for model robustness. To evaluate the accuracy of the developed models, common statistical indices such as ROC, TSS, and Kappa were used. These indices represent the model's ability to discriminate between erosion and non-erosion points, overall model accuracy, and agreement between the model and observed data, respectively. By comparing these indices for different models, the optimal model for predicting wind erosion distribution was selected.  Finally, the generated maps were classified into four classes based on erosion sensitivity: low, moderate, high, and very high. These maps graphically illustrate areas with a high potential for erosion and can be used as a management tool for planning and implementing erosion control measures. Among the 25 environmental variables examined, 23 variables with a Variance Inflation Factor (VIF) of less than 10 were selected for the final model to minimize multicollinearity. This careful selection of variables significantly improved model accuracy and reduced errors in the results. Based on the stability results, the ensemble models (ESMs) demonstrated the best overall performance in predicting all forms of wind erosion, including ergs, low-density small dunes, high-density small dunes, high-density coarse-grained ridges, medium-density medium-grained ridges, medium-density fine-grained ridges, surface erosion in the form of ridges, removal and transport traces, and sandy areas, with respective accuracies of 0.982, 0.968, 0.921, 0.984, 0.992, 0.814, 0.816, 0.893, and 0.821. These models achieved very high accuracy in all three evaluation criteria. By combining multiple machine learning algorithms, this model has high generalizability and can be applied to various environmental conditions. Some of the most important environmental variables affecting the potential of wind erosion include drainage density, hydrological groups, frequency of rocks and pebbles, percentage of bare soil, slope, average air temperature, and vegetation cover. These variables indicate that various natural and human factors influence the wind erosion process. Modeling results indicated that nebkas and ergs were primarily distributed in the central part of the basin, while surface erosion was more prevalent in the northwestern and western regions. These spatial patterns highlight the influence of various topographic, vegetation, and climatic factors on wind erosion distribution within the basin.  The significance of this research lies in its ability to empower natural resource planners and managers to accurately identify critical areas susceptible to erosion. By utilizing this information, targeted management practices, such as afforestation, windbreak establishment, mulching, and proper rangeland management, can be strategically implemented in vulnerable areas. These measures not only mitigate wind erosion and conserve soil but also contribute to improved air quality, enhanced biodiversity, and ecosystem sustainability. Machine learning models offer numerous advantages for wind erosion assessment. These models provide a powerful tool for accurate and generalizable prediction and monitoring of soil erosion. Furthermore, they enable the evaluation of the impact of climate change and land use changes on wind erosion, facilitating long-term strategic planning for sustainable water and soil resource management. Additionally, these models can optimize the costs of implementing management measures and enhance their effectiveness. In conclusion, this study demonstrates the significant potential of machine learning models in assessing and managing wind erosion. By employing these models, a deeper understanding of complex wind erosion processes can be achieved, enabling the design and implementation of effective and adaptive management strategies to conserve natural resources and protect the environment.

Soil conservation is of paramount importance for sustainable development, food security, and environmental protection. Understanding soil erosion is considered as a critical practice for soil conservation programs worldwide. Currently, soil erosion is increasingly recognized as a serious concern for sustainable agriculture, water resources management, and modern civilization. Soil erosion poses a significant threat to soil, ecology, and humanity since the long-term productive capacity of soil is profoundly impacted by the degradation and washing away of topsoil and soil organic matter. In arid regions like Iran, soil erosion is a major crisis and can be considered as one of the critical challenges for agricultural development, natural resources, and the environment. The high sediment load entering rivers from upstream areas leads to increased water turbidity, reduces the lifespan of dams due to reservoir sedimentation, and negatively affects water quality and biological activity.

The Mukhtaran watershed is located in the South Khorasan province of Iran, covering an area of 2421 square kilometers. It lies on the southern slopes of the Bagheran Mountains and encompasses a diverse range of landforms, including mountainous and hilly terrain, vast plains, and playa devoid of vegetation. Annual precipitation in the Mukhtaran region varies between 150 millimeters in the lowlands and 220 millimeters in the highlands. The average annual temperature is 14.3 degrees Celsius; the average minimum temperature is 5.6 degrees Celsius, and the average maximum temperature is 22.7 degrees Celsius. In this work, at the first stage, various base maps including the drainage network, basin slope, geology, geomorphology, soil, and land use were extracted. Then, the map of work units was identified and classified. Subsequently, by interpreting the available aerial photographs, the extent of rill erosion forms was separated on the primary map. Through field visits, the locations of rill, gully, and streambank erosion were recorded using the GPS. The number of points for each erosion type and severity level were as follows: Rill erosion:  Low: 55 points, Medium: 90 points, Severe: 58 points, very severe: 66 points, Gully erosion: Low: 62 points, Medium: 69 points, Severe: 63 points, Streambank erosion: Low: 95 points, Medium: 107 points, Severe: 12 points. Based on a review of previous studies and considering the nature of water erosion and the available baseline information resources in the region, 25 important and influential variables on water erosion were identified and their maps were prepared using various sources. Employing the available information, 25 environmental variables were considered for model generation, including 5 physiographic variables, 2 climatic variables, 4 hydrologic variables, 8 soil variables, 4 land cover variables, and 2 geological variables. The data was divided into two groups for training and validation with a ratio of 70 to 30. The model was repeated five times to evaluate its stability. The performance of the model was evaluated using the metrics ROC, KAPPA, and TSS.

Based on the validation analysis results, the best model for slight to very severe rill erosion was the ensemble model (ESMs) with the accuracies of 97.0%, 85.0%, 90.0%, and 98.0%, respectively. For slight to severe gully erosion, the ensemble model (ESMs) also performed best simulation with the accuracies of 88.0%, 96.0%, and 96.0%. Finally, for slight and moderate streambank erosion, most models performed well, but the ensemble model (ESMs) had the highest accuracies with 94.0%, 98.0%, and 99.0%, respectively. In all forms of erosion, the ensemble model (ESMs) performed best simulation based on all three coefficients, at an excellent level.

Mukhtaran watershed is severely faced with the problem of land degradation in various forms of erosion such as channel erosion, rill erosion and stream bank erosion, and this is the reason that not only the economy of this area is affected, also the natural environment and ecosystem related to it. In this study, different machine learning approaches with random sample partitioning were applied to estimate the most accurate vulnerable areas with the maximum possible accuracy. An examination of the relative importance of all environmental factors in each of the three major types of water erosion in the study area showed that physiographic factors, geological factors, land cover, and soil had a significant impact on the geographical distribution of water erosion forms in the Mukhtaran watershed. These results are consistent with the studies that have used support vector machine as an effective tool for mapping erosion susceptibility in watersheds. Overall, it can be concluded that machine learning models are effective and novel approaches for land use planning and erosion risk management.

Vegetation cover is one of the basic and key ecological factors in arid regions, which will be severely affected by the drought process. As a result of these impacts, there will be increased intensity of soil erosion and soil degradation. Therefore, the present research was conducted to evaluate the process of drought and its impact on changes in vegetation in Sarakhs county in Khorasan Razavi province. In this research, the climate data of precipitation and monthly temperature of synoptic and evapotranspiration stations in a period of 21 years (2000 to 2021) were used to estimate the standardized precipitation-evaporation-transpiration index (SPEI). To investigate the vegetation changes, the images of ETM+ and OLI satellite sensors and the difference vegetation index (NDVI) were used. The index SPEI was calculated in the software environment R. Prediction of future vegetation cover changes is done using the Markov model and in the IDRISI-TerrSet software environment. Finally, the correlation between two indicators of drought and vegetation was compared with a linear regression test. The results of this research showed that the highest level of drought in the years 2000, 2010, and 2020 occurred in Sarkhas city at moderate and severe levels. The investigation of changes in vegetation cover in 2030 showed that the soil without cover increased compared to the year 2020, and the area of land with other classes, including vegetation with medium, high, and thin density, has decreased. Correlation analysis between vegetation cover and drought indicators showed that the highest correlation value of 0.59 at the significance level of 0.01 has been observed, and in the years 2000 and 2020, the correlation coefficient was higher at 0.52 and 0.51, respectively. The results of examining the relationship between the NDVI vegetation cover index and SPEI climatic index indicated the existence of a positive relationship between these indices.

Landfill development in a place can potentially affect the physical, biological, economic and social factors of the place. This study aims to identify the appropriate landfill in Bardaskan city located in the west of Khorasan Razavi province and also to investigate the suitability of existing landfill for this use. To do this, first, environmental criteria affecting the selection of suitable landfill development site were identified, mapped, and weighed using the AHP process. Weighing results showed that geological criteria, distance from drinking wells, and distance from Bardaskan city with weights of 0.173, 0.172, and 0.124, respectively, are the most important in identifying suitable site. Aftertaht, landfill site selection was performed using Multi-Criteria Evaluation by WLC method. The WLC suitability map was classified into five categories, including very low to very high suitability, and based on this, potential landfill sites were identified. According to the results, 5% of the basin had very high suitability and 32% had high suitability for developing landfill site. Also, the existing landfill site based on the suitability map indicates that it is in a good category. In the next step, in order to choose the best site for the landfill, the Iranian Leopold matrix was prepared and completed for each of the selected zones. The evaluation results showed that among the investigated areas, the current landfill - with remedial measures - is the best place for landfill site in this basin. The proximity of water resources (rivers and aqueducts), a long distance to the city of Bardaskan, and unsuitable soil were the most important reasons for removing of other areas.

Land subsidence, as one of the environmental hazards, is happening in many countries of the world. This phenomenon, if not properly managed, can cause irreparable damage. In this regard, Iran, as one of the countries that has been facing the risk of land subsidence for several decades, will probably be affected by the damage of this phenomenon in the near future. Damages including; Damage to agricultural lands, buildings, roads, bridges, pipelines, etc. Various activities such as; Mining, excessive extraction of underground water, etc., have intensified the process of land subsidence to the point where it has become one of the most important hazards of geomorphology. Identifying the boundaries, the pattern of land subsidence and estimating its intensity will play a significant role in the management and control of this phenomenon. Therefore, it is necessary to take measures to prevent further problems. Therefore, the important goal of this research is to evaluate the subsidence of the Kashmer plain with the radar interferometry method and its relationship with the groundwater drought. Maybe the results of this research can help the executive managers and planners of land and soil resources in the field of protection and management of water resources to prevent land degradation.

In this research, radar interferometry was used to monitor subsidence, and Kendall and Pettit's time series analysis tests were used to evaluate groundwater drought. Also, to calculate the density of wells, kernel density estimation was used, which transforms and determines the position of points in space in a continuous density function in the studied area. Finally, to determine the correlation coefficient and covariance between the existing rasters, as well as some statistical parameters such as the minimum, maximum, average and standard deviation values for each raster, Band Collection Statistic analyzer, which is considered a part of multi variate analyzers was used. Two SENTINEL satellite images were used to determine the amount of subsidence in the target area: 1- The Master image was taken on 04/04/2017. The variable image of 03/25/2021 Slav was also used with a time span of about 4 years and using Land subsidence was calculated from SNAP software. The PSI index was used to determine the level of groundwater drought. This index can be used for all piezometric levels.

The results of the radar interferometric analysis showed that the subsidence in the studied area was due to excessive water withdrawal from wells and underground water sources. The cause of subsidence around the wells was due to the lack of equipment and the entry of particles into the well. The results of changes in the time series of the PSI index showed that it is insignificant at the probability level of 5% in Kalate Rahim and Khalil Abad wells and significant in the rest of the wells. In the studied area, the lowest water level drop is related to the wells of Khalil Abad, Kalate Rahim. Khalil Abad well has had a drop in water level from 1996 to 2006, and from 2006 to 2021, the PSI index had an upward trend. The analysis of PSI data up to 2019 showed that the groundwater drought in the Kashmir plain has reached its peak over time, and from 2019 to 2014, the conditions have somewhat returned to normal conditions. In addition, subsidence is developing from the western areas such as Khalilabad, which have been involved in this issue in the past, towards the central areas and the center of Kashmar plain. The highest density of wells in the study area is in the central areas towards the western areas of the plain.The study of the land use map of the region also showed that the majority of the density of wells is related to agriculture and irrigated agriculture, which includes 76% of the exploited wells.

The obtained results showed that in different regions of Kashmir Plain, in the period of 4 years (2017 to 2021), there was about 46 to 84 cm of land subsidence, which means that annually in different areas between 11.5 and 21 cm of subsidence can be observed. On the other hand, the process of subsidence has reached the central areas from the west of the Kashmar plain, and the highest intensity of subsidence can be seen around the city of Kashmar. The time series of PSI groundwater drought index is insignificant in Kalate Rahim and Khalil Abad wells and significant in the rest of the wells. Regarding the relationship between the amount of land subsidence and the severity of groundwater drought, the highest amount of subsidence is located in the areas that are in the minimum historical conditions of the PSI index and include the highest fluctuation of subsidence in the region. As we approach the normal conditions of the region in terms of the PSI index, the amount of subsidence also decreases, which indicates a strong connection between groundwater drought and land subsidence. The future perspective of the dangers caused by this phenomenon is very difficult and even impossible, considering the vast dimensions of the damage it causes to the fields of natural resources. Therefore, the custodian bodies, including the country's natural resources and watershed management organization, the General Directorate of Natural Resources and Watershed Management, the Jihad Agriculture Organization, the Regional Water Company of Khorasan Razavi Province, have a very heavy responsibility in managing the crisis and improving the conditions.

Desertification is one of the major environmental, socio-economic problems in many countries of the world (Breckle, et.al., 2001). Desertification is actually called land degradation in dry, semi-arid and semi-humid areas, the effects of human activities being one of  the most important factors (David and Nicholas, 1994). Sand areas are one of the desert  landforms, whose progress and development can threaten infrastructure facilities. The timely and correct identification of the changes in the earth's surface creates a basis for a better understanding of the connections and interactions between humans and natural phenomena for better management of resources. To identify land cover changes, it is possible to use multi-temporal data and quantitative analysis of these data at different times (Lu, et.al., 2004), therefore, one of the accurate management tools that causes the application of management based on current knowledge, these studies Monitoring is done using the mentioned data. The use of satellite data and ground information in such studies has caused many temporal and spatial changes of phenomena to be well depicted, which can be beneficial in better understanding  and  interaction with the environment and ultimately its sustainable management  and development. To obtain and extract basic information, the best tool is to use telemetry technologies, which by using satellite data, in addition to reducing costs, increases accuracy and speed, and its importance is increasing day by day in the direction of sustainable development (Alavi Panah, 1385). Since field studies in the field of spatial changes of sandy areas of this city are difficult and expensive to repeat, facilities such as simulating these areas with expert algorithms and artificial intelligence can be used to investigate and monitor critical areas at regular intervals. Accurate and economically appropriate. Therefore, in this research, with the aim of investigating the effectiveness of these models in the periodic changes of the sandy plains of Ferkhes plain, two algorithms, perceptron neural network and random forest, were chosen, and the reason for choosing these models is the ability to model according to the existing uncertainties, interference Fewer users and insensitivity of the model to how the data is distributed.

The progress and development of the sandy areas of the Fern Plain depends on three factors, climatic, environmental and human. Therefore, the input variables to the expert and artificial intelligence models were chosen to cover these three factors. Therefore, factors such as drought, the number of dusty days, as well as vegetation index were entered into the model as dynamic variables, and environmental factors such as soil, elevation and altitude, geology, slope and direction were entered into the model as static variables. The statistical period investigated for the changes of wind erosion zones was considered to be 15 years from 2000 to 2015, based on this time base, qualitatively homogeneous and reconstructed meteorological data and images A satellite was selected and processed in 5-year periods (2000, 2005, 2010 and 2015). Modeling of the changes of sandy areas was done using two algorithms of perceptron neural network and random forest in MATLAB software environment. To choose the best neural network structure, a large number of neural networks with different structures were designed and evaluated. These neural networks were built and implemented by changing adjustable parameters (including transfer function, learning rule, number of middle layer, number of neurons of middle layer, number of patterns). One of the most common types of neural networks is multilayer perceptron (MLP). This network consists of an input layer, one or more hidden layers and an output. MLP can be trained by a back propagation algorithm. Typically, MLP is organized as a set of interconnected layers of input, hidden, and output artificial. The accuracy of these networks was checked by the statistical criteria calculated in the test stage, and finally the network that had the closest result to the reality was selected as the main network. The main active function used in this research is sigmoid, which is a logistic function. Then by comparing the network output and the actual output, the error value is calculated, this error is returned in the form of back propagation (BP) in the network to reset the connecting weights of the nodes (Chang and Liao, 2012). Other evaluation indices MSE, RMSE and R were used as network performance criteria in training and validation. The selection of Fern plain as a study area is due to the high potential of this area in the advancement of sand areas, for this purpose, 8 effective factors in the development of these areas were investigated. These factors were entered into the model in the form of three dynamic indices and five static indices.

In evaluating the results of modeling algorithms, dynamic variables in all periods were introduced as the most important factors in the occurrence of wind erosion and the advancement of sand areas. The diagram of the importance of predictor variables is presented in Figure 7. The results show that the vegetation cover index ranks first in all periods, the drought index ranks second in 2000 and 2015, and the dust days index ranks third in these two years. Meanwhile, in 2005 and 2010, the dust index and drought index ranked second and third respectively. Among the static variables used in this research, the height digital model variable was ranked fourth in 2000 and 2010, and in 2005 and 2015, geological and soil variables were important. In almost all studied periods, the direction factor is less important than other factors, which can be removed from the set of variables required for modeling to predict sand areas.

Climate change affects extreme events such as floods and droughts. This research was conducted in the watershed of Kashmar City in Khorasan Razavi province and the output of general circulation models from the CMIP5 model series was used to check the performance and validity of these models in predicting the climatic parameters of precipitation, average temperature, maximum temperature, and minimum temperature during the period 1989-2005. The best model was selected using the evaluation indices of the determination coefficient, the root means squared error, the absolute mean error, the correlation coefficient, and the mean squared error. Finally, the BCSD method was employed to downscale the data for three time periods near future (2020-2038), middle future (2039-2069), and far future (2070-2100) under the IPSL-CM5A-MR model and future emission scenarios (RCP2.6, RCP4. 5, RCP6, RCP8.5). The Mann-Kendall statistic test was used in order to investigate the trend of changes in monthly, seasonal, and annual rainfall and temperature variables. The results of the model evaluation indices for four climate models indicated the highest performance of the IPSL-CM5A-MR model in simulating precipitation and temperature with a high correlation coefficient and relatively low error indices compared to other models. The results of the investigation of the rainfall trend showed that in the far future, the rainfall component will be completely decreasing and it will have the largest decrease under the RCP8.5 scenario with a value of -41.12 mm. The average temperature is also increasing in all months and the maximum increase in the average temperature in the far future under the RCP8.5 scenario is 7.58 °C. The amount of minimum temperature and maximum temperature increases in all months, and the maximum increase of minimum temperature in the far future interval under the RCP8.5 scenario is 8.35 °C. Furthermore, the maximum increase in the maximum temperature in the far future interval under the RCP8.5 scenario is 7.09 °C.

Ecosystem services are fundamental for human well-being and are the basis of rural livelihoods, particularly for poor people. Rainwater harvesting can serve as an opportunity to enhance ecosystem productivity, thereby improving livelihoods, human well-being, and economies. Rainwater harvesting has been shown to create synergies between landscape management and human well-being. These synergies are particularly obvious when rainwater harvesting improves rainfed agriculture, is applied in watershed management, and when rainwater harvesting interventions address household water supplies in urban and rural areas. Rainwater harvesting has often been a neglected opportunity in water resource management: only water from surface and ground water sources is conventionally considered. Managing rainfall will also present new management opportunities, including rainwater harvesting. Improved water supply, enhanced agricultural production, and sustainable ecosystem services can be attained through adoption of rainwater harvesting with relatively low investments over fairly short time spans (5-10 years). Rainwater harvesting is a coping strategy in variable rainfall areas. In the future climate change will increase rainfall variability and evaporation, and population growth will increase demand on ecosystem services, in particular for water. Rainwater harvesting will become a key intervention in adaptation and reducing vulnerabilities. Moreover, in addition to increasing public knowledge about the ecosystem function of these systems, basic steps should be taken in the audit, evaluation and application of local knowledge of rainwater harvesting in the country.

Due to the fact that ecosystems are more fragile and sensitive in arid and semi-arid regions of the world, the phenomenon of floods and the resulting damage and loss will be more severe in these areas. The subject of river canals and their morphology is one of the key topics in engineering and river management, which can be used to obtain a useful set of information about the geometric shape, bed shape, longitudinal profile, cross sections, over time. Default geometric relationships in hydrological models such as Kineros2 are based on field measurements in US watersheds and cause uncertainty in model results. Therefore, the purpose of this study is to determine the regional statistical relationships between the width and depth of the canal with the area of the upstream watersheds for employing in hydrological models such as Kineros2, and the width and depth of the canal with other basin characteristics. The data in this research are mainly topographic maps of Neishabour Bar watershed. Thus, according to topographic maps, preliminary studies were carried out to identify sub-basins. In this study, the basin is divided into 34 sub-basins that involve Lar Formation (in 16 sub-basins), and marl (in 18 sub-basins), where, 27 sub-basins in upstream are non-orchard lands. The linear and nonlinear regressions and equations were studied and evaluated using the software such as Excel, SPSS, Curve Expert, and XLSTAT. The results of correlation of physical parameters with canal width and depth, nonlinear regression analysis and analysis of variance in the relationships between canal depth/width with upstream area in the whole basin (R2=0.58 for canal depth and R2=0.14 for canal width), in upstream Lar formation, in downstream and non-orchard lands, showed a greater impact of the upstream acreage on the canal depth relative to the width (due to the higher coefficient of determination and less error). Furthermore, the separation of sub-basins in terms of geological formation and the presence of orchards had a significant effect on improving equations and reducing errors. The greater impact of the canal depth than the width from the upstream area is mostly related to successive droughts and the absence of flash floods in the area to change the canal depth, while the width of the canals has been mainly a function of human manipulations on the river bed. Stepwise linear regression analysis also showed a higher correlation between canal depth than the canal width with the physical parameters of the basin (R2=0.85 and R2=0.77, respectively).

Iran has geographically located in an arid and semi-arid climate in most regions. Precipitation and its distribution in such regions cause irreparable damage by creating seasonal floods. This study presents a suitable model for optimizing watershed management and flood control in order to reduce flood risks. To reach to this purpose, the concept of time-area diagram in HEC-HMS hydrological model as well Single Successive Sub-watershed Elimination (SSSE) is employed to simulate the flood hydrograph corresponding to the design precipitation for each sub-watershed. According to SCS model for estimating flood discharge and kinematic wave for flood routing, the curve number and Manning's roughness coefficient were calibrated and identified as the most effective parameters. After evaluating the different search methods and objective functions, the univariate gradient as best search method and the Nash-Sutcliffe as the best objective function was selected due to the highest consistency of the simulated discharge in the three events. Finally, the model was validated for 2 storms and the Nash-Sutcliffe values ​​were calculated as 0.948 and 0.892, respectively. After calculating the peak discharge of each sub-watershed, the effect of each on the output flood production was determined using F and f flood indices. Then, isochronic surfaces of the watershed were extracted using three methods and the spatial distribution of the sub-watersheds in the area was investigated. The results revealed that the level of 0.75-1 located in the middle part of the watershed is posed as the first priority. Also, it is colcluded that the surfaces near the outlet have played a much smaller role in peak discharge. In general, from the outlet to the upstream and middle parts of the watershed, as travel time level increases, the effect of sub-watersheds on peak flow discharge increases.

This study aimed to evaluate and compare the estimated rainfall of GPM meteorological satellite with ground stations using non-parametric Kolmogorov-Smirnov in Gorganrood catchment, Golestan province. In this study, 16 stations were used with the statistical period of 20.03.2014 -20.03.2016. The daily rainfall data of the GPM satellite with a spatial resolution of 0.1 degrees were collected. Data analysis was performed in the MATLAB environment. The results showed that ​​in Bagh Salian, Dashte Shad, Ghochamz, Hagh-ol-Khaje, Zaringol stations, the P-value ​​were higher than the α significance level and were 0.83, 0.36, 0.11, 0.95, 0.33, respectively. Therefore, it showed that the difference between satellite precipitation data and ground station observations was not significant. The results also showed that (0.95) P-value was the highest correlation of GPM data with observational records in Hagh-ol-Khaje station. The lowest level of compliance with the value of 0.001 is related to Shirabad station. Therefore, to improve GPM satellite products, especially in watersheds with the wide spatial distribution and temporal variations of precipitation, calibration of satellite precipitation products should be on the agenda to improve their accuracy in measuring daily precipitation. The Kolmogorov-Smirnov test was performed for the first time to examine the correlation of daily GPM satellite data with observational data, and there was a relative correlation between satellite data and observed ground station records in all stations where the H0 hypothesis was accepted

Land use and land cover (LULC) is one of the most important factors that affect the desertification risk in this study, the desertification sensitivity of the Mokhtaran basin in South Khorasan province was estimated by integrating LU/LC scenarios with the MEDALUS environmentally sensitive areas (ESAs) model to predict the desertification risk. The four main MEDALUS criteria including soil, climate, vegetation, and management were examined to assess the sensitive areas to the desertification. Land use maps were categorized using the Landsat satellite imageries of TM, ETM+, and OLI sensors for 1987, 1998, 2003 as a past scenario, and 2015 as a current scenario. Land use maps for 2025 and 2035 were produced as the future scenario based on the simulation of CA-Markov models. The validation results confirmed the model accuracy by calculating the kappa coefficient of 0.95. The land use map was predicted for the years 2025 and 2035 based on the transition rules and a transition area matrix. The results showed that the rainfed area was reduced by 68.29 km2 and the agricultural land was increased by 25.35 km2 during theperiod. In the protection area of playa-bare lands, the changes showed this area was increased by 26.86 km2. The rangeland has also experienced positive changes with an increase of 18.83 km2. Compared to the current scenario, the desertification trend in the future scenario was positively predicted by increasing the area of critical areas from 30.9% to 48.7% over 20 years. The most susceptible lands to desertification were known as playa-bare lands.

Performance and control are two important components in any executive activity and their purposes are to correct and improve the functions and reduce wasting resources for better productivity and adoption of appropriate techniques that ultimately lead to the development of programs. Therefore, the present study aimed to simulate underground water flow and investigate the effects of artificial water supply projects on the underground water resources of Frizi plain in Chenaran using a mathematical model and MODFLOW code. The studied aquifer model is the most sensitive to changes in hydraulic conductivity and discharge of wells and shows less sensitivity to changes in surface water supply due to rainfall. The artificial water supply scheme of Jamab in the control scenario had caused an annual increase of 453717.6 meters2 of water to the aquifer. The maximum volume of water entering the aquifer system through the artificial water supply of Jamab in 1995-2012 was equal to 1.8 million cubic meters, and according to the control scenario had increased by 1.35 million cubic meters, this amount of supply had increased the average of the water level of the aquifer of Frizi plain compared to the control scenario in the forecast period in 2014, 2015, and 2016 by 0.09, 0.11 and 0.17 m, respectively. The minimum volume of water entering the aquifer through the artificial supply of Jamab in 1995-2012 was in 1995 and 2002, which was approximately equal to 170 thousand cubic meters, which compared to the control scenario had decreased by 283 thousand cubic meters, the estimated revenue in the system obtained during the operation period until 2012 and was equal to 57.5 billion tomans. The profit to cost ratio of the project in terms of all revenues from project ecosystem functions and production was about eleven, which showed that the project was profitable until 2012.

Soil erosion is a complex process with adverse environmental impacts. Therefore, in this study, to assess the severity of soil erosion in Iran watersheds, the validated data set of the Global Soil Erosion Modeling project (GloSEM database) were used. This project includes baseline scenario (2015) and future forecasts (2070) of soil erosion with the effects of climate change in three scenarios RCP2.6, RCP4.5 and RCP8.5 on rainfall erosivity factor (R) and the effects of land use change in three combined scenarios SSP1-RCP2.6, RCP4.5 SSP2- and SSP5- RCP8.5 on vegetation (C) and soil protection (P) factors. The results showed that the vulnerability of central, southern, and eastern watersheds due to climate change and land use chaange was higher than other watersheds. In these areas, even without considering the effects of climate change, the trend of soil erosion changes in the baseline scenario (2015) with SSP2-RCP4.5 and SSP5-RCP8.5 scenarios has been increasing, but declining in humid or semi-humid areas in north and west. Considering the combined effects of land use and climate change, the trend of soil erosion changes between the baseline scenario (2015) with the scenarios of SSP1-RCP2.6 (with an average increase of 184%), SSP2-RCP4.5 (with an average increase of 243%) and SSP5- RCP8.5 (with an average increase of 341%) has been increasing in all watersheds except Aras watershed. The results of the study indicate that the vulnerability of more than 10 times in the central watersheds of Iran due to climate and land use changes by 2070.

Simulation of the rainfall-runoff process for planning and management of water resources and watersheds requires using a conceptual optimized hydrological model. Models of different types provide a means of quantitative extrapolation or prediction that will hopefully be helpful in decision-making. Recently, the application of models has become an essential tool for understanding the natural processes that have occurred in the watershed. KINEROS2 (Kinematic runoff and Erosion), or K2, originated at the USDA Agricultural Research Service (ARS) in the late 1960s as a model that routed runoff from hillslopes is represented by a cascade of overland-flow planes using the stream path analogy proposed by Onstad and Brakensiek (1968), laterally into channels. Manual calibration of hydrological models has been used since the early 1960s, but due to its complexity and being time-consuming, automatic calibration has been available since the end of the 1960s. Auto-calibration needs an appropriate objective function, search algorithm, and a criterion to complete the algorithm. The particle swarm optimization (PSO) algorithm, due to its flexibility, easy implementation, and high performance, has been favored by many researchers in recent years. This method has a high rate of convergence and suitable computational cost.

In this study, the hydroPSO package was employed to optimize KINEROS2 (K2) parameters applied in the Bar watershed, Neyshabour, Iran. The hydroPSO package in R software environment was utilized to implement the PSO optimization algorithm. The possibility to develop R capabilities by adding the produced packages by the users is one of the most important specifications of this software. The statistical measures used in model validation analysis were model bias (MB), modified correlation coefficient (rmod), and Nash-Sutcliffe Efficiency (NSE). These metrics are the most common evaluation criteria in the literature. The capability of the model in water discharge estimation can be assessed by MB, while rmod signifies the differences both in hydrograph size and shape. In this work, 16 parameters have been introduced as the effective parameters on flood simulation by K2. These parameters were calibrated using the hydroPSO optimization package within R environment, which benefits from a parallel processing capability and a higher speed of computations, as compared with other software environments like MATLAB. The common parameters in the calibration process involved in the main code of K2 program include Ks, n, CV, G, and In. In this study, by changing some codes in K2 through the FORTRAN programming language, calibration parameters were increased by 16 parameters. Therefore, the response of a watershed to the variations of these parameters, separated for channel and plane, can be well evaluated. Due to semi-distributed simulation of K2, changing the amount of each parameter was done through “relative changes” in the initial value using a multiplier approach. Five storm events were utilized in hydrograph simulation, as well.

Results indicated the better efficiency of K2 based on the event 1992/03/31 with the coefficient of determination and Nash-Sutcliffe Efficiency (NSE) of 0.96 and 0.96, respectively. The events dated 1991/03/07, 1991/05/11,1992/03/16,1994/12/04, respectively, with the NSEs of 0.90, 0.90,0.89, and 0.43, showed the excellent, excellent, excellent, and good fitness of simulated flow compared to observed flow, respectively. Sensitivity analysis established that the parameters Ks_c, n_c, Rock, G_p, Ks_p, Por_p, and Sat were the most effective parameters in K2 calibration, respectively. The posterior distributions of some parameters such as n_c and Smax appeared to be more sharply peaked than other parameters which established less uncertainty in hydrological modeling. Visual inspection of boxplots showed that for 8 out of 16 parameters (Ks_p, n_c, G_p, In, Rock, Por_p, Por_c, and Sat), the optimum values found during the optimization coincided with the median of all the sampled values; confirming that most of the particles converged into a small region of the solution space. Dotty plots showed that the optimum values found for n_c define a narrow range of the parameter space with a high model performance. On the other hand, the model performance was more impacted by the interaction of Ks and n parameters. Correlation analysis revealed that the highest linear correlation between NSE and K2 parameters was obtained for the parameters Ks_p, Ks_c, n_p, CV_p, G_c, In, Por_p, Dist_p, Dist_c, Smax, and Sat.

In comparison with manual calibration, the HydroPSO R package could compensate for the shortage of K2 proficiency, due to the lack of enough observed rainfall records, in hydrologic modeling of semi-arid watersheds. Thus, it can be successfully integrated with the K2 model to harness the combined benefits of a distributed hydrological model and flexible computing capability of the open-source R software. However, the performance of HydroPSO in K2 calibration should be assessed for several semi-arid watersheds which have the similar conditions to Bar watershed.

Valuable studies have been conducted in recent years to collect water from impermeable urban levels in some parts of the country. Nevertheless, the background of the studies in this field has been poor in south Khorasan province, Iran. This study aims to help urban planners and managers to recognize the potential of water harvesting. The purpose of this study is to identify and prioritize the sub-catchment covered by the rock outcrops playing an important role in runoff production. This work was carried out via the modeling of runoff by empirical methods and the SWMM model in Qaen urban watershed. Accordingly, it is possible to determine the amount of water available for supplemental irrigation of the urban green space and thereby provide a suitable extension pattern for similar areas. The irrigation in the urban park is done by well, which is a major problem for Qaen, as an arid area. About 54% (248.4 ha) of the basin surface has been populated by the 90% rock outcrops that has high runoff potential and can be useful in planning in terms of high potential of runoff production. Considering the existing facilities and the number of park trees, an approach is needed. The water shortage for supplemental irrigation of this park will compensate, if only 10% of the annual runoff equivalent to 11800 m3 can be harvested and stored during the months with no precipitation.

Precipitation is one of the most important factors affecting water and energy balance in the world and important meteorological variables. To accurately estimate precipitation, various methods are used, including the direct use of meteorological ground station data and direct observations, the use of remote sensing satellite data, or the use of interpolation methods based on geo-statistical methods. Therefore, developing innovative approaches for accurate estimation of precipitation in areas with inadequate or inadequate data is critical. The use of radar remote sensing technologies in the accurate estimation of precipitation is crucial as the most important factor affecting water and energy balance in areas with unsuitable and inadequate data. Therefore, this research was conducted to evaluate GPM-IMERG satellite precipitation data and compare it with the data of observatory stations in the Golestan province-Gorganroud basin.

To do this research, after obtaining the GPM satellite data and processing it, we performed a comparison between half-hourly daily satellite data set with the ground-based (stability and normal) observational data. Concerning the spatial (0.1*0.1) and temporal (daily and half-hourly) resolutions of GPM-IMERG satellite data, we employed enough and valid ground-based rainfall records dated 20/03/2014-20/03/2016 (for daily series) and 20/03/2014-21/09/2016 (for half-hourly series). To assess the accuracy of GPM data in rainfall estimation, some statistical indicators such as FAR , CSI , POD , RBias and some other validation indicators were used.

The results showed that the half-hour rainfall IMERG records with CC values equal to 0.05-0.23 and CSI equal to 0.20-0.52 were relatively acceptable. Validation of GPM satellite rainfall data using MAE, RMSE, and MBE statistical indicators has also been relatively acceptable. Based on the validation analysis of daily records, the RBias index showed the highest level of accordance of GPM data with observational data at 0.74 , and the lowest level corresponding to 2.27, that belongs to Nodeh station. The POD index also showed that Nodeh and HagholKhajeh stations had the highest and lowest correspondence with ground stations with the values of 0.5 and 0.25, respectively. The values of the CSI index in all stations were calculated to be between 0.13 and 0.22, which were related to Zarrin Gol and Shirabad stations, respectively. Based on the values of the FAR index, it was observed that the lowest value of FAR in Bagh Salian and Zarringol stations was 0.64 and the highest value was 0.80 in Shirabad station. Therefore, to improve the data obtained from the IMERG algorithm, especially in arid regions with the extensive spatial distribution and temporal changes in precipitation, satellite precipitation products should be calibrated to improve their accuracy in measuring daily precipitation.

In this study, the calculation of statistical and matching indicators was performed for the first time to compare half-hour data of the GPM satellite with observational data. It was found that the IMERG algorithm of the GPM satellite is relatively consistent with the recorded values of ground stations daily, as well. Given the FAR values at all stations, it can be said that there is a relative correspondence between satellite data and observed data from ground stations. POD values also showed acceptable performance of this satellite's data. The results of this study also showed that there was a relative correlation between the data of ground stations and GPM satellite data. Therefore, considering the non-evaluation of precipitation data of the GPM satellite system with data of ground stations in many regions of Iran, including the study area of Gorganroud, the results of this study can be very useful for innovation and increasing the efficiency in water resources management.

Watersheds are considered as the main unit of planning for sustainable development in many management issues. Evaluating the ecological capability of watersheds and their ranking according to different ecological criteria for prioritizing water management operations is one of the important issues in watershed planning and management. In this study, the Fuzzy-TOPSIS integration method, was chosen to be usen in the Mokhtaran watershed. In this work, 13 environmental criteria, including water resources quantity, flood intensity, soil conservation, surface water quality, groundwater quality, forage production, potential evapotranspiration, groundwater quantity, upstream resources, vegetation composition, water erosion, wind erosion, grazing capacity and groundwater quality of upstream resources were estimated and used. The study area was divided into three blocks of northern heighlands, southern highlands and plain. Each of them has its own criteria, according to the characteristics and potentials of each block. Then, the criteria of each block were weighted by AHP method and each of the 13 environmental criteria was fuzzified. After the fuzzification of the criteria and the weighting of the criteria of each block, the main problem of each sub-basin was determined according to previous studies in the watershed. Then, accordingly, the sub-basins were grouped in each block. Moreover, based on the Likert spectrum, the relative importance of options (sub-basins) was identified as triangular fuzzy numbers relative to each criterion. Finally, by entering all of the inputs in Fuzzy Topsis Solver 2013, the coefficient of proximity of each sub-basin was estimated and based on the nearest coefficient, the priority map of the watershed management plan for the watershed of Mokhtaran was extracted. According to this map, the sub-basins M8, M13, M15, M16, M21, M22, M25, M26, M28 (in the northern heights of the area), M2, M42, M43, M51 (within the southern heights of the area), M32 , M34, M46, M'13 and M'14 (within the plain of the basin) were considered to be in the highest priority and as critical hydrological units. The sub-basins M4, M5, M'4, M'5 (in northern highlands) and M1 (in the plain) were placed at the lowest priority over the study watershed.

Land use/cover changes (LU/LC) are considered as one of the most important issues in natural resource management, sustainable development and the environmental changes on a local, national, regional and global scale. Changing uses into each other and changing permissible uses into impermissible uses such as changing agricultural lands into residential regions or changing rangelands into eroded and low-yielding dry farming lands are always considered as importand issues in natural resources. Detection of the patterns of the land use changes and prediction of the changes in the future to carry out suitable planning for optimal utilization of uses in natural resource management reveal the need for modeling spatial and temporal changes of LU/LC. This study aims to assess the efficiency of the integrated model of Markov chain automatic cell (CA-Markov model) in simulation and prediction of spatial and temporal changes of Land use/Land cover (LU/LC) in Gorgan-rud river basin by applying three-dimensional Pentius-Melinus analysis in calibration of land use changes by using three assessment indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit as new indices in the assessment of the accuracy of CA-Markov model.

 In this research, the Earth observing sensor images of Landsat-5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) acquired from the U.S. geographical site dependent on the U.S. Geographical Survey (USGS) were used to predict land use changes by using the integrated model of Markov chain automatic cell in Gorgan-rud river basin. Seven land use classes were separated for Gorgan-rud river basin including forest land class with the use code 1, agricultural land class with the use code 2, rangeland class (a mixture of shrubbery,langeland,agriculture) with the use code 3, water bodies class with the use code 4, barren land class (barren, rangeland, agriculture) with the use code 5, residential and industrial region class with the use code 6, streambed class with the use code 7. In this study, object-oriented classification method and  Support Vector Machine (SVM) algorithm were used to classify Landsat 5 and 8 satellite images and extract the land use classes of Gorgan-rud river basin. Segmentation scale  in this algorithm on a 50 unit scale (SL 50) was selected to classify the satellite images of 1987, 2000, 2009 and 2017. The assessment of the accuracy of Support Vector Machine algorithm in the object-based classification of satellite images was done by representing overall accuracy, Kappa cefficient, user accuracy, producer accuracy, commission error and omission error for four study periods. To understand how the changes in the region were created during the period of the study three decades and which classes had the area expansion and which classes had the area decrease, changes in the limits of the classes were revealed and percent of the changes in each class were obtained by using the classification maps and IDRISI software. CA-Markov model predicts the changes of different groups of LU/LC units based on spatial neighbourhood concept, transition probability matrix. Preparing land suitability maps is necessary to predict land use changes so that spatial changes can be controlled for each use by probability rules via filtering suitability maps. Validation of Markov model was performed by using three-dimensional Pentius-Melinus analysis with three assessment indices of Figure of Merit, Quantity Disagreement and Allocation Disagreement.

 Support Vector Machine algorithm in the classification of the land use based on object-oriented showed that the highest rate of commission error and omission error were observed in rangelands and agricultural lands with 19.12 and 18.55 percent respectively in the land use map of the year 2009. The lowest accuracy of the producer with 71.49 percent belongs to the rangeland use class in the land use map of the year 2009 and the lowest use accuracy with 71.45 percent belongs to agricultural land use class in the land use map of the year 2017. In keeping with the obtained results, the highest positive change belongs to the agricultural land use increase and the highest negative changes belong to rangeland and forest land use decrease during the period of three decades from 1987 to 2017. The highest forest land decrease with 4.8 percent, the highest agricultural land increase with 5.3 percent, the highest rangeland decrease with 9 percent, the highest barren land increase with 4.6 percent and the highest residential and industrial land increase with 0.8 happened during the periods of 2000-2017, 1987-2017, 2009-2017, 2009-2017, and 1987-2017 respectively. After validating the predicted land use chnges in CA-Markov model, based on the analysis of the 5 existing states in three-dimensional Pentius-Melinus analysis, the CA-Markov model with the accurate prediction of simulation of 89.92 percent showed the high efficiency of CA-Markov model in simulation process. After the implementation of the CA-Markov model analysis on the obtained land use map from the classification of the satellite images, one transition probability matrix and one transitioned area matrix were created. In predictions made by using CA-Markov model in 2017 to 2033, the most changes relate to barren and forest land expansion decrease to 16966 and 6961 hectare respectively and in contrast to the use decrease, rangeland, residential and agricultural land expansion increase will be observed to 20397, 3913 and 3825 hectare respectively.

 Detecting land use changes by using LCM tool for the period of three decades 1987-2017 in Gorgan-rud river basin showed that the forest, agricultural and residential use has had significant changes in this region. The obtained results of the prediction of the land use changes during the coming eighteen years by using the integrated model of Markov chain automatic cell following the detected changes by LCM tool show that we will face extreme deforestation phenomenon in this area. Investigation of the obtained results from the implementation of the future use network model by using Markov transition estimator showed that the future use changes can be predicted based on the existing environmental conditions showing that the agriculture will extremely increase in Gorgan-rud river basin during the coming eighteen years. Thus we can protect water and soil resources with comprehensive and long-term management and prevent the degradation of these valuable resources. Three indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit in three-dimensional Pentius-Melinus analysis had an important role in representation of the accuracy rate and calibration of the land use classification and the land use prediction corresponding with the obtained results from the carried out studies concerning the accuracy assessment with indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit. The results of the studied land use changes by using LCM tool and the integrated model of Markov chain automatic cell during the period of 1987 to 2035 show the degradation of more than 24309 hectare of the forest lands and agriculture increase in an area about 62421 hectare indicating human interfernces and deforestation we face in this area.

This study was conducted to simulate water balance components at field scale, predict soil moisture profile, and grain yield in irrigated wheat fields in Neyshabur plain. In this regard, three farms were selected in different parts of the plain. AquaCrop input data including air, soil, and crop parameters were collected at each farm separately, then, the required model parameters and wheat crop data were calibrated. Root mean square error (RMSE), model efficiency (EF) and prediction error (Pe) were used to evaluate the model performance. The results of moisture simulation in soil profile showed that the model correctly simulated moisture content at different depths and times. The statistical parameters used for evaluating efficiency of the model at the calibration stage for simulating soil moisture in all farms were 0.027<RMSE<0.032, 0.80<EF<0.91, and 3.5<Pe<14%. These values at model validation stage were 0.025<RMSE<0.031, 0.82<EF<0.94, and 2.7<Pe<12%. The minimum and maximum percentages of model simulation error for grain yield and water productivity in all farms managed by the farmers were 4-8.8% and 4.6 to 9%, respectively. According to the results of the research, AquaCrop model can simulate soil moisture content, grain yield, and water productivity with acceptable accuracy under similar field conditions.

Rivers are the first environmental forms that show a relatively rapid response to changes in the bedding or fluctuations in the outflow of the bed. Regarding the fixed-effect photos of the rivers relative to the occurrence of abnormal changes-mainly due to tectonics-can be explained by analyzing them using morphotectonic indices in relation to the occurrence of these changes ( Ramiez Heerea, 1998). Exploring the dynamic processes that affect the formations of Earth and its landscape can be achieved by the Active Tectonics or Active Geomorphology Tectonics One proven and widely used technique is the Morphometric analysis, which helps understand the development of the geomorphic features in the young mountain belt. Morphotectonics can be considered as the knowledge of the study of shapes and patterns created on the ground by tectonic processes .)keller and pinter., 2002.( Mousavi (2006) reviewed the active tectonics in the northwest of Birjand. He points out that structural and morphological factors have been calculated, due to the structural arrangement in the area, the rate of the process of rising in the central part is higher. The principal objective of this study is to Evaluation of active area tectonics using morphometric parameters for the using classification of geomorphological indices considering the geological and physiographic study of watershed and tectonic analysis and geomorphology. Morphometry indicators of active tectonic is concern with the study of the geomorphological conditions of the study area, physical environment and landscape characteristics of the study area which includes drainage pattern, stream order, slope and aspect map.

In this paper, Birjand plain is divided into 36 sub-basins for ease of computing and comparing the results of using indicators which is from sub- basins 1B to 22B of northern part of plain of Birjand, which includes the mountain Shekarab, and from sub- basins 23B to 36B is the southern part of Birjand Plain, which includes the mountain range of Bakhran The data used in this research includes the topographic maps 1: 25000, geological map 1: 100000, fault map 1: 100000. the digital elevation model data (DEM of 30 meters of the region) for mapping the location of the region. We use the 10.3Arc GIS software and modules such as 3D Analyst, Editor, Analysis Tools, and also Google Earth software.

The importance of morphotectonic studies is to achieve critical results that are often unavailable through other methods or require a great deal of time and cost. For this purpose, in the course of morphotectonic studies, indicators are being studied that can be of great help in reaching the outcome of the research.The results of calculating the indexes are such that the highest rate of AF in the northwest is the lowest rate of tilting in the north of the area. The T index is the most abnormal case in the northwest of the region. S index is the highest value in the west of the region and the lowest value in the north-central region. The maximum amount of Smf is in the north of the area and its lowest value is in the northeast of the basin. Calculating the VF index in the mountains of Shekarab showed that the highest number of valleys is located in these areas. hypsometric integral is the highest elevation in the northeast and the lowest in the north, the highest is Dd in the north and the lowest in the northwest. The highest value of Te is in the northeast of the basin and the lowest is Te in the northwest. The highest value of C is in the northwest and the lowest in the north. The highest value of Re is in the north and the lowest value of Re in the northwest. The Iat parameter is used to analyze geomorphologic indices and summarize their results in order to determine the relative status of tectonic activities in the region.The Iat parameter indicates that the northern plain of Birjand is morphometric changes in the active class, and in the future will have new changes.

The results obtained from the study of geomorphic evidence show a relative classification of tectonic activity that is useful for studying and identifying the region. The geomorphic indicators used in this project reflect the tectonic activity of the region. In general, the results of geomorphologic indexes indicate that the study area of the study is active in the field of nautical construction, one of the reasons for which is the trusted fault and the extension of slip, and the activity level of the newly constructed motions in The waters of the basin are more active in this regard than anywhere else.

Morphotectonics expresses the relationship between geomorphology and tectonics. The plain of Birjand is located in eastern Iran. The purpose of this study was to identify the physical parameters of the study area also, the performance of these parameters is based on changes occurring on the surface of the earth, this purpose, field data and topographic maps have been used and the following results are achieved. The AF range varies from 28.11 (southwest) to 0.61 (southwest). The range of changes in T is from 0.72 (southwest) and 0.23 (southwest). The highest Smf is 1.91 (southeast), the lowest is 1.02 (southwest) of the basin. The calculation of the VF index showed a slight neo-tectonic upsurge. The lowest value is S 1.05 (west) and the largest is 1.16 (west) of the region. The lowest value of the index is Re 0.19 (southern part) and the highest value is 0.72 (south). Hypsometric integral is calculated; the highest value is 0,505 (Southeast) and the lowest value is 0,166 (Southwest). The Dd index is (South East) with the lowest 1.59 and the Southwest 2.25. The highest Te (southeast) is 2.55 and the lowest (southwest) is 0.47. The highest value of the index C (southwest) is 0.62 and the lowest (southwest) is 0.44. The parameter Iat shows; the plain of Birjand is located in the active degree of morphometric changes, and these active areas are more in the southern range (Bagheran mountain).

Sarakhs is strategically and geopolitically important in Khorasan Razavi province and the country. Existence of Khangiran Refinery, Sarakhs Special Economic Zone, Silk Highway, Sarakhs Great Transit Customs, etc ... It has led to the development and prosperity of the city that has added to the city's population in recent years. On the other hand, the three of the wind erosion crisis, and progress of the wind erosion zones in the region due to drought and the persistence of dust storms, are the most important threats to the strategic locations of the region. It is important for planners and managers to be aware of changes in wind erosion zones over a period of time and their relationship to climate. One of the most cost-effective ways to determine sandy zones variations is to use satellite images and related indicators. Among the many vegetation indices, NDVI is one of the global vegetation indices. On the other hand, global warming has had a significant impact on vegetation growth in recent years and has had a significant impact on vegetation dynamics. The purpose of this study was to compare the capability of detecting satellite and climate indices in the changes occurring in Sarakhs plain. That drought has detrimental effects on environmental and human resources, In this research, we are trying to identify the trend of expansion of sandy areas that is a serious threat to the resources available in Sarakhs city, using drought, dust storm and satellite indicators

In this research required data including implemented projects, general statistics and information of the area, layers and topographic maps, meteorological data, satellite images, field sampling and ... were collected and were analyzed with statistical software, satellite image processing and geographic information systems software. The statistical term for studying changes in climate and satellite indices is 15 years, from 2000 to 2015, based on this time base, meteorological data were selected for qualitative, homogeneous and reconstructed Landsat satellite images. The climate indices calculated in this study were SPI and DSI index. That daily data of dust, horizon and daily precipitation data of synoptic stations, rain and evaporometer stations were used to calculate these indices and the satellite indexes include the NDVI and SDI, which use the Landsat ETM + satellite imagery. The results of the calculation of these indices were compared and analyzed.

 Investigation between the values of climate and satellite indices showed, according to climate indices, the years 2000, 2005, 2007 and 2010 are considered as dry years, with NDVI and vegetation levels also decreasing. In 2015, the vegetation level is at its highest level and this means that the amount of vegetation has increased as the rainfall changes and the climate changes to normal or wetter compared to other years. The results of the indices were also compared with the changes of the sandy area for the four periods of 2000, 2005, 2010 and 2015. Results show that the 2005 drought occurred after four years of climate-normal conditions in the region therefore, the area of sandy zones was less than other years of statistical period. Comparison of the surface of the sandy zones in the four studied periods shows in 2015, despite the SPI index showing normal natural climate conditions and the DSI index showing the lowest number of days of dust in the region during the statistical period, the area of sandy areas has increased dramatically due to years of continuous drought. 

 The study of drought index shows that during the study period 2007 and 2015 are the driest and most productive years, respectively. This is also confirmed by the results of the dust index That highest and lowest number of dust storms occurred in the Sarakhs plain in 2007 with 98 days of storms and 2015 with 16 days of storms, respectively. A comparison of land use changes in the Sarakhs plain shows that the highest agricultural land development is 7% in 2010 compared to the previous period and while in 2015 the area has fallen by 12 percent due to droughts. On the other hand, rangeland degradation in 2010 reduces land use by 15% in 2010 Caused by the droughts of 2007-2010. Reduced rangeland and bare land could be another reason, including dryland cultivation in the area. Calculation of the NDVI index for the period 2005-2010 also showed that the amount of vegetation decreased significantly (From 125/69 to 111/53 km2) While in 2015 as an almost normal year, the vegetation rate increased according to the NDVI index. (Approx. 60 Km2) which indicates the effect of drought on the vegetation of the region. In this study, the ability of detecting two climate indices and one satellite index, to study changes in wind erosion zones in Sarakhs plain was studied. The results show that the NDVI index is in good agreement with the SPI and DSI climate indices. As a result, the level of sandy zones varies according to the drought situation, the intensity of dust storms, and the level of vegetation cover in the years studied.

Land cover and land use are an important variable in natural land processes. Land use change in environmental protection programs and natural resource management plays an important role in the intensification of natural crises such as floods. The Gorganrood River basin in the Golestan province has historically experienced land use conversion. In this research was selected for land use classification using Landsat 8 OLI satellite images of the 25 June 2017. The goal of this study is to assess the accuracy of two approaches, pixel-based supervised classification and the object-oriented one base on quantity and allocation disagreement indexes. The accuracy assessment results indicated verified that for land use mapping the SVM algorithm using a 50 pixel segmentation in the object-based classification having a quantity disagreement of 2.03, an allocation disagreement of 4.58, and an overall accuracy of 92.65% and a kappa coefficient of 0.91 was more accurate than other algorithms in the object-based classification and other algorithms in the pixel-based classification. Based on this algorithm, the lowest of omission and commission error showed in forest lands and residential and industrial areas of 0.58% and 1.59% respectively. The highest of producer and user accuracy showed in forest lands and the water body of 99.44% and 99.41% respectively. The largest area of land use in the Gorganrood River basin is related to the Barren/Rangeland/Cropland class of 314110 ha. Finally, the SVM-SL50 algorithm in the object-based classification is suggested as an optimal classifier with a high accuracy for classification of land use classification maps in order to manage natural resources in Golestan province.