hadi memarian khalilabad

One of the most important factors of desertification is wind erosion, whose importance and danger on a global scale is no less than water erosion (Refahi, 2010). The emergence of sand dunes in some countries such as China is one of the examples of progress and development of the desert, and in some countries exposed to desertification, this process is less important and other processes such as salinization, destruction of plant resources, etc. are important. It can be said that one of the natural forms and symbols of desert areas is the sand dunes that occupy a considerable area of the desert compared to other natural forms of the desert (Mashhadi et al. 1385). The total area of these hills and sandy areas that occupy or threaten the outskirts of cities, villages, communication lines of the country and infrastructure facilities is estimated to be 13 million hectares, of which about 5 million hectares are active hills. And the rest includes fixed or fossilized hills (Khatsasi et al., 2014). One of the most obvious characteristics of sand dunes is their movement, which causes the loss of non-economic land uses, natural vegetation, etc. to increase the movement and displacement of the dunes. (Dang et al., 2000). Therefore, the timely and correct identification of the changes of such complications creates a basis for a better understanding of the connections and interactions between humans and natural phenomena for better management of resources. Having three wind erosion crisis centers, namely Yaztepe wind erosion crisis center and Mohiniran border post, Gonbadli and Abmal wind erosion crisis center and Samad Abad wind erosion crisis center, in recent years it has been severely affected by wind erosion and expansion There are sand dunes that have caused many problems for people.

Based on the studies of the atlas of wind erosion centers in 1379 by the country's forests and pastures organization, the studies of three critical wind erosion centers named Yaztepe-Gonbadli, Abmal and Samadabad have been identified in it, and each center is divided into three regions., transportation and sedimentation have been separated into low to high intensities. These studies are related to the last 20 years, which were compared and analyzed with the areas extracted in 2000 and 2005. The sand areas prepared in 2000, 1250 square kilometers and in 2005, 976 square kilometers have a good match with the locations of wind erosion crisis centers. In the review studies of the critical centers of wind erosion of ferns, which was carried out in 2019 by the engineers of the Tak Sabz consultant, the critical center of wind erosion of Chale Zard was identified and it was suggested by this consultant to prepare additional studies in the areas of wind erosion in 2010. In this research, which is related to the last ten years, the direction of the sandy areas is towards the center of Chale Zard, and this confirms that despite the prevailing wind direction in the area, which is from the northwest to the southeast. is, the advance of the sand flats is towards the west of the region, and it is possible that the only reason for that is the presence of the Tajen river and the agricultural lands in the east and northeast of the region, which hinder the advance of the sand flats. It goes parallel to the direction of the prevailing wind in these areas. The area of sandy areas in 2010 has increased by seven percent compared to 2000. The area of sandy areas in 2015 is 1356 square kilometers and its front is towards the northwest of the studied area. As mentioned earlier, there are three critical centers of wind erosion in the region, and identification studies and suggestions for preventive and curative measures have been presented in these studies. Therefore, priority has been given to parts of the region that were not previously in the territory of the city's sandy areas, and according to the research conducted, they are currently located in this area. Based on this, in the north, north-west and west part of the study area up to the location of Samadabad wind erosion center, considering that the majority of the area is poor pastures, there are 21 villages, 177 kilometers of communication roads and 10 km of railway is located in this area, it is considered as the first priority of management planning to prevent the development of sandy areas. The second priority in the central part of the studied area is between the critical foci of wind erosion from Gonbadli-Abmal to Samadabad, which is due to the conversion of pasture lands into wet fields, wrong agricultural patterns, lack of management and excessive livestock grazing, causing pastures to become plains. It has become sandy. If management plans are not implemented to control desertification, in the not-too-distant future, critical conditions will be reached on a wider scale, which will lead to transregional environmental problems. The priority of three areas is according to the location of the critical centers of wind erosion, Yaztepe-Khangiran, Gonbadli-Abmal and Samad Abad, and restoration projects for the harvesting, transportation and sedimentation areas of these centers have been presented in past studies.

The results of this research indicate the different changes of sand areas in 5-year periods. Although the area of these zones has increased during the studied period, which is 15 years, but in some periods, due to different climatic conditions, the area of the zones has decreased. According to the results of this table, the area of sand dunes in 2000 was equal to 125,000 hectares, which reached 135,000 hectares in 2015, and almost 10,000 hectares have been added to the area of these zones, but the area of sand dunes in the year it decreased in 2005 and 2010 and increased in 2015. The area of wind deposits decreased in 2005 and 2015 and increased in 2010. All these results confirm the non-uniformity of changes in these areas in different years, which depends on various factors, including the climatic conditions of the region. Also, the prioritization of the sandy areas in Sarkhes city was based on the threats to the resources and strategic areas of the city, which include population areas, important regional facilities, touristic, cultural, economic, agricultural areas, and communication routes. Was performed. According to the areas of wind erosion in 2015, a total of 24 km of Sarkhs-Mashhad railway, 295 km of communication roads, 25 villages, 4801 hectares of hand-planted forests in the region and important areas such as Almas Airport, Nazbiran Refinery, Khatun bridge and the special area of ferns were located in these areas.

Complex nature of porous media complicates any prediction of their hydraulic properties. To demonstrate shortcoming of hydraulic conductivity models predictions, the concept of tortuosity was introduced. Since there is no measured data of tortuosity, and tortuosity has a direct relationship to hydraulic conductivity, so in this study we aimed to develop a general mathematical relationship to determine tortuosity. An optimization code were run in MATLAB R2014a software, using Monte Carlo algorithm, aimed to minimize Root Mean Square of Logarithmic Deviation (RMSLD) between calculated hydraulic conductivity values based on Shepard (1993) and van Genuchten (1980) models, to determine tortuosity on different water contents for 69 soil samples of UNSODA database with a wide range of soil textures. Considering fractal concepts, we developed a linear equation empirically to determine hydraulic tortuosity as a function of effective saturation, pore fractal dimension, porosity, inverse of air entry pressure and soil water content, covering whole ranges of degree of saturation. Based on results, calculated values of tortuosity were greater than proposed values by Shepard about 30%. To evaluate developed equation, statistical parameters of Root Mean Square of Logarithmic Deviation (RMSLD) and Akaike’s Information Criterion (AICc) was adopted for 17 different soil samples. According to the calculated statistical parameters, using developed equation to estimate tortuosity has improved the results of Shepard’s method significantly. Totally, the results show that, despite the developed equation has a relatively complicated structure, in terms of the compromise between accuracy and complexity has an acceptable performance.

Planning and optimal use of resources and controlling and unprincipled changes in the future, requires studying the extent of change and destruction of resources. In fact, planners for principled decisions must have a full knowledge of land use, detection, prediction of land use change and land cover in order to better manage natural resources in the long time. The aim of this study was to evaluate the accuracy of different supervised classification algorithms of basic and object-oriented pixels in land use extraction in Samalghan watershed in three periods of time 1987, 2002 and 2019. The results showed that the support vector machine algorithms for the images of 1987 and 2019 and the neural network for the 2002 image in the pixel-based classification method have the highest overall accuracy and kappa coefficient. Also, the most obvious change that can be seen by comparing the prepared user maps is the change in the level of land uses with the growth of residential areas, thus this expansion has been continuously associated with a decrease in rangeland land use. Thus, from the years of 1987 to 2019, the residential land use area increased by more than 9.197 km2 and dryland lands during these years increased by 130.89 km2, irrigated agricultural lands also increased from 44.45 km2 and Rangeland use has also decreased by 272.3 km2.

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Unmanaged land use change is one of the major challenges of the 21st century. The hydrological effects of land use and vegetation management are evident in the changes in runoff depth, minimum discharge, maximum discharge, soil moisture, and evapotranspiration. Land use changes are recognized as one of the main drivers of the hydrological changes in the catchment area. By developing urbanization,land use changes increases the risk of floods with decrease in time of discharge reaches at the peak. Land use changes may bring some problems in the region's climate, water cycle, and its natural habitat. Every year, population growth is accompanied by the increased demand for lands due to agricultural and housing purposes, while the industrial development leads to the loss of fertile lands and changes in the water balance of the region. Land use changes along with the increased urbanization, agricultural activities, and forest degradation are among the major drivers of changes in the water balance. The usual strategy for analyzing the spatial distribution of land use changes is first to identify the pattern of quantitative and qualitative changes, and then changing processes are examined. An estimation of the future prospect of land use changes would be an effective step in the sustainable water resources management to deal with the crises caused by the overdevelopment of land uses. Land cover maps play an essential role in the analysis of land use changes.
This study was conducted with the aim of a quantitative detection of land use changes in the watershed of Kardeh Dam located in Mashhad, Iran. Mashhad, with a total area of 3,288 square kilometers and 3 million inhabitants, is the second largest and populated city in Iran. The increased and unplanned development of this metropolis and the establishment of industries in Mashhad Plain have led to the change in the land use of all plain watersheds, which is not consistent with the existing water resources and has created serious risks for the management of this city. Therefore, the present study aims to investigate the land use changes in this basin in different categories and time periods from two spatial and temporal dimensions and at three levels of time, category level, and intensity based on the method presented by Aldwaik and Pontius (2012).
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Kardeh Watershed, with an area of about 54,425 hectares in Northeastern Iran, is located in 42 kilometers of the north of Mashhad (the second largest metropolis of Iran). This watershed supplies a part of the drinking water of Mashhad and also irrigates the agricultural lands located in the lower areas of Kardeh Dam. Nowadays, many researchers use the assumptions and innovative method of Pontius and Malizia (2004) to analyze their research results. Huang et al. (2007) used the intensity analysis to study the pattern of temporal and spatial variations in the land cover and land use in the catchment basin of Jiulong River in China. Aldwaik and Pontius (2012) expanded this method for the analysis of the intensity of changes to investigate the changes at three levels (1) time level land use changes; (2) the level of gain and loss for each category level; and (3) the intensity of land use changes and their transition from one level to another. The land use change analysis over the time is the highest level of the application of this method. The intensity analysis is a quantitative form for calculating the differences between the classes, and it is summarized in a transition square matrix with rows and columns having the same levels. With the analysis of intensity in each level, the degree of deviation between the observed changes and the assumed level of intensity can also be obtained (Aldwaik & Pontius, 2012).
The spatial data obtained from the satellite images of landsat archives to extract the land use data. One of the most common methods to categorize land cover is the supervised MLC method (Dean et al., 2003; Richards et al., 2006). The algorithm of this method is according to the likelihood of assigning a pixel to the target class (Lillesand et al., 2004). To categorize land use, the two methods of fuzzy and maximum likelihood were combined in this method. With the field investigations and the existing land use maps, the study area was classified into 5 category: 1) Rangeland; 2) Irrigated farming and orchards; 3) Rainfed farming; 4) Rock outcrop and bare land; 5) Residential. After defining land uses with the help of points registered at the field investigation stage and choosing the educational samples, the pixels on color images representing the reflection of the intended use or coverage were selected. To provide the ground truth map, the random samples were used; the accuracy of maps was evaluated through the pixel to pixel comparison of the classified maps with the ground truth.
By the classification of the images, their changes were investigated over the time from 1987 to 1998, 1998 to 2008, and 2008 to 2016 at the three levels of time interval, category, and transition. At each level, the stationary patterns of land use were compared at different time intervals. The intensity analysis is done based on a mathematical method which compares the observed temporal intensities with the uniform intensity.
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The results of the Kappa coefficient which calculated for all periods, show that all the Classifications are in an appropriate range and do not have a significant difference. The evaluation of the classification accuracy indicates the low level of accuracy of the producer’s on the BARR category in 2008, which is 77%. Other criteria have acceptable values, and intensity analysis across three time intervals shows that the highest quick changes in land use occurred over the period of 2008-2016, which is mainly due to the rapid growth of urbanization and the increased migration to the central city of Mashhad during this period. The rapid growth of urbanization and the increased demand for food has caused rapid changes in the land use in adjacent basins, which may justify the increasing land use changes in the studied area. In all time slots, the intensity of categorical land use changes indicates that Rangeland and Rainfed Farming category have the most changes compared to the entire period which can be mainly due to the reduction of recent rainfalls and droughts.
In all time slots, Rangeland accounts for the highest stationary. By comparing the different time slots, the middle period 1998-2008 has the highest and the period 1987-1998 has the least stationary. During the time interval of 1987-1998, the highest gain occurred from the category of Rained Farming to the Rangeland, which might be mainly due to favorable climatic conditions and low population density. During the time interval of 1998-2008, the highest loss occurred from the Rangeland category to the rainfed farming category, which is perhaps related to the sudden growth of Mashhad and the increase in the population of this metropolis due to excessive migration and consequently increased demand for food from neighboring basins.
Transition intensities representing the gain of target categories show that during the period 1998-2008, changes in the Bareland was significant, and these changes were towards into Rangeland. During the time interval of 2008 and 2016, the two categories of Rainfed Farming and Bareland, which cut the uniform line of change, have transited to Rangeland. Transition intensities representing the loss of target categories indicates the significant changes in the Rainfed Farming category, which is targeted at the Rangeland category in the period of 1987-1998. During this time interval, changes took place from Rangeland to Rainfed Farming. Changes from the Irrigated Farming category in the time interval of 1987-1998 to the Rangeland was the target, and it transited to Rainfed Farming category during 2008-2016. During the time interval of 1987-1998, the change from the Rainfed Farming category was limited to the Rangeland category; however, in the time interval of 1998-2008 and 2008-2016, this change took place from the Rainfed Farming category up to the two categories of Irrigated Farming and Rangeland. At all the time intervals, the Bareland category has transited to Rangeland and inverse.
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In the three studied periods, the results illustrate that the most changes and fluctuations are among the rangeland, irrigated and rainfed farming categories without a regular pattern. Rangeland is the only dormant category for both gains and losses in spite of being involved in most of the changes. As the Rangeland category has the highest area in this watershed, it plays an important role as a potential resource for conversion to other uses. The most important reasons include weaknesses in the enforcement of laws, population growth and the increased demand of food and agricultural activities, the development of infrastructure in rural areas including electricity which has led to expand pressured irrigation systems for converting rangelands to the rainfed and irrigated farming in high slopes. In the years affected by the drought, these developed lands could not be exploited, under rainfed and irrigated farmming, and then converted to uncultivated and rangelands. These sudden and non-regular changes in the steep slopes have increased the erosion and intensity of the uncovered bare land changes, which then would lead to severe floods.

The increasing flooding trend in recent years suggests that most of the country's regions are vulnerable to invasions of periodic and destructive floods. In this aspect, many cities, villages, industrial and agricultural facilities and residential areas are prone to flood occurrence, as well. Therefore, the basic identification of flood process within the catchment area is one of the most important measures in flood control and the mitigation of damages. The main objective of this research is to investigate and identify the flood areas and the effect of watershed management on flood peak discharge in the outlet of the Bar watershed, Neyshabour, located in Razavi Khorasan province.
Material and For this purpose, the basin was divided into 20 subbasins and the physical properties of the whole basin and subbasins were determined using the geographical information system oodand in a digital format. Then, by using the HEC-HMS hydrologic model, the corresponding flow discharges were calculated for each subbasin. Then, by successively deleting subbasins at each model runtime, i.e. Single Successive Subwatershed Elimination method (SSSE), the whole basin water discharge was calculated after the flood routing in the main streams without the subbasin by using the kinematic wave routing approach, Thus the effect/share of each subbasin in the production of flood is identified. Also, the flood discharge of the basin was calculated in the basin area unit and the flood index (f) was the basis for the priority of the basin.
In calibration process, two parameters of curve number and manning coefficient were selected as the most effective parameters on flood discharge. The high Nash-Sutcliffe coefficient in flood events showed that calibration of the model in the watershed basin was appropriately done. The results showed that the subbasin B1 (in the northern part of the watershed) in the return periods of 50 and 100 years had the hieghst peak discharge of 38.9 and 44.1 cubic meters per second at the outlet of the subbasin, and the subbasins B11, B13 and B19 (in the western parts of the watershed) showed the minimum peak discharge. Also, according to the index (f), in flood plains with return periods of 50 and 100 years, the subbasins B4 and B3 (in the northern half of the watershed) ranked first and second, respectively and the subbasins B6, B11, B12, B13, B14 and B19 (in the southern part of the watershed and in the eastern and western parts of the watershed) showed the lowest priority in terms of their participation in basin flood. In subbasin B1, the highest level of peak discharge has been observed in the highest level of biological operations, ranging from 41.27 to 44.73 percent. On the other hand, the results showed that the higher the proportion of the biological activity to the subbasin area, the more obvious the role of these projects in reducing peak discharge. According to the study, the role of structural activities in reducing the flood peak is lower than biological activities, and increasing the number of structures along the river route will reduce the peak peak area of the subbasin.
By investigating the effect of biological activities and the construction of gabion check dams on the flood discharges, it can be said that the role of biological activities in reducing peak flow and flood volume is much more effective than structural activities (construction of gabion). Therefore, the CN factor is an effective and controllable factor for flood discharge of the basin, on reducing peak flow.

Geographical looking at the distribution of activities and the spatial establishment of productive communities in territorial space without any doubt nomadic communities are considered as one of the productive points, particularly for producing beef and other dairy products. Nomadic communities in the process of socio- economic transformation of beneficiary systems also have been troubled with some living problems that in are associated often with lack of welfare needs. In order to equip and develop nomadic system, different plans have been done around countries. One of the discussed options is organizing nomadic centers, in this plan proper places are considered consistently for different needs of nomadic settlements in order to settle them and strengthen their economic foundations. In this paper has been tried location selecting for nomadic center to be considered by use of indicators and fundamental effecting components (environment _ ecological, socio_ economic and spatial) in the life of nomadic according to a case study winter resting of Ab Talkh e Sarakhs in form of documentary survey. The study case is located in one of the in northern Khorasan Razavi nomadic ecosystems, Sarakhs province. In this regard, the approach of equipment and development of resources seeks to prove that the sustainable organization of nomadic on one hand provides physical space for their living and settlement, and on the other hand, ensures stability of nomadic economic system.

Land cover information can be used in hydrologic modeling to estimate the value of surface roughness or friction. The objective of this work is comparison of two different methods to provide land cover maps using Landsat images to estimate surface roughness in the manning equation and curve number (CN) in SCS method in the Kameh watershed located in the North of Torbat Heydarieh. At the first, Radiometric and Geometric correction performed on ETM+ data. Then with field surveying, The land cover classes were defined and training areas were selected. All of the bands with the exception of band 6 were used in classification. Because of low accuracy of village and road classes, These classes were removed from classification process and for entering these classes in the final map, information from the other layers of GIS system were applied. Results of this work show that in the fuzzy method using 3 layers in classification, The overall accuracy is 75.12% and kappa Index is 0.63. While those of fuzzy method using 2 layers in classification and maximum likelihood method are (73.3%, 0.6) and (72.39%, 0.59) respectively. Therefore the fuzzy method using 3 layers in classification is recommended.