نشریه جغرافیا و مخاطرات محیطی
پیاپی 34 (تابستان 1399)

Increasing land-use changes have increased the need for managers and experts in land resources to understand how changes and developments have occurred as well as possible future changes for good and efficient policymaking and decision-making (Parker et al., 2003; Akbari et al., 2019). Today, land use change is important in terms of environmental change and has got the attention of scientists and decision makers (Mas et al., 2014).The increasing destruction of natural resources in many parts of the world is a serious threat to humanity. Therefore, desertification, which is one of the manifestations of this destruction, is currently a problem in many countries, including developing countries (Parvaneh, 2009). At present, remote sensing technology with the highest speed and accuracy is a suitable tool for assessing land use changes in order to monitor desertification (Hashemi-Nasab et al., 2018; Davari et al., 2018). Different land use models have been designed and used by researchers to predict land use changes in several studies such asDavolbit and Morari (2012) in Sudan, Lamchin et al. (2016) in Mongolia, Halabian et al. (2016) in Iran, Isfahan, using land use modeling, assessing and predicting desertification changes.Studies show that in recent years, land cover around the world has undergone many changes that can severely affect the environment and natural resources. Given that land degradation leads to desertification, this issue intensifies the importance of studying land cover changes, considering the strategic importance of Sarakhs region. In the present study, the temporal-spatial changes of land use in intensity of desertification have been investigated and modeled using satellite images.2. Study AreaThe study area of ​​Sarakhs is a small part of the great Gharakhum basin which is located in Sarakhs city. The border of the region is in the western and southwestern part of Bazangan and Shurluq mountains, in the northern part is the Iran-Turkmenistan political border and in the eastern part of Tajan river. Geographically, the ferns range is located at 60 degrees 15 minutes to 61 degrees 10 minutes east longitude and 35 degrees 50 minutes 36 degrees 40 minutes north latitude. This area is naturally prone to desert expansion, which is exacerbated by its proximity to the Qaraqoom desert, creating a dry and cold climate. Wind erosion is more common in arid climates, where soil productivity intensifies drying conditions. Sarakhs region suffers from critical desertification conditions due to arid climate, low rainfall, unfavorable soil, land use change and increased wind erosion. The study area, as the epicenter of wind erosion crisis, has created environmental problems. Therefore, due to acute environmental and human problems and strategic location, Sarakhs was selected as the study area.

In this study, Landsat TM and ETM+ sensors were used to study land use dynamics. Geometric, radiometric and atmospheric corrections were performed on the images. Satellite imagery from 2000 to 2015 was monitored to prepare the land use map and examine its changes over 15 years. In this study, Landsat satellite imagery classification maps were first prepared using the maximum likelihood method for the mentioned years. Finally, the user map for different years was prepared. The LCM Land Change Modeler was used to model land use change in 2030. Modeling in this model was done in three stages. In the first stage the influential variables were selected and introduced to the model and the role of each of them according to the Kramer coefficients was determined. In the second stage, the potential transfer map was prepared based on the effective variables and land use maps of the previous periods and in the third stage, the future land use map was prepared. The validity and reliability of the modeling and classification maps are based on the estimation of the kappa coefficient and overall accuracy. Common validation maps of classification lands and changes model prediction maps were used for validation. In this way, the predicted map of the model was compared with the land use map produced by the supervised method and obtained by calculating the standard kappa coefficient, position accuracy status and pixel quantities of each class.

Map of land use classes of Sarakhs area in six classes of water level, agricultural lands, poor, and bay rangelands, medium and rich rangelands, wind deposits and residential areas and Landsat satellite imagery classification for the years under study were prepared. To determine the percentages of land use changes in the study period, each of the classes obtained in different years was individually placed on the sum of the classes obtained the following year, and then the number of changes of each of these uses was calculated in 2015. To import the variables into the land change modeler, it is necessary to map the variables. Altitude variables, distance from residential areas, distance from the road, distance from river and distance from agricultural land were selected as input variables of the model. It is possible to estimate the correlation of each variable with the existing land use and its ability to predict land use changes by calculating the Kramer coefficient. After revealing land use changes over the years 2000 to 2015, the number of changes in each transfer was predicted using the Markov Chain and the total land-use change map in the LCM model for 2030. To validate the power of the LCM model to produce land use maps for 2030, the 2000 and 2005 maps were first used to predict the 2015 map. To make this prediction, the change probability matrix and the probability area matrix of changes were prepared and based on defined sub-models and change probability maps, the 2015 land use map was prepared. Comparison of the model predicted map with the land use map prepared by the supervised method and calculation of standard kappa coefficient, accuracy condition for position and quantity of pixels of each class were obtained. The 2015 ground truth map was then compared with the simulated map of the land-change modeler. The accuracy of the model was evaluated based on the Kappa index. The error matrix results showed that the kappa coefficient of variation in the land change model is 0.85. Using the multilayer Preston neural network approach, the user change map for 2030 was obtained. According to the results, the process of reducing the average and rich rangelands and agricultural lands continues. The extent of wind deposits will also increase, covering about 17% of the area in 2030.

In the present study, land use changes in the north east of the country over the three-time periods 2005 to 2015 were investigated using Landsat satellite imagery and the ability to predict land-use changes based on the LCM modeling approach. Comparative results of land use maps in the three mentioned periods show the level of change in all land uses. The degradation of medium and rich rangelands in the third period was higher than the degradation of the second and first periods, indicating more severe degradation in the 2015–2010 period. One of the reasons for most of the degradation of rich rangeland during this period is the reduction of rainfall, drought and surplus livestock in recent years. The most varied land use changes over the years can be attributed to a decrease of about 3 percent in medium and rich rangelands, an increase of three percent in poor and barren rangelands and an increase of about one percent in agricultural lands. The total area of ​​the sand has increased by more than 1% over the whole period. The decrease in rangeland and the increase in waste and agricultural land indicate the inappropriate use of these lands for agricultural purposes. Damage to rangelands, increased levels of agricultural land with the degradation of land, use of land outside their ability and potential have led to increased desertification. During this period, the area of ​​residential areas grew by about 0.2%. The results of predicting changes over the next 11 years using the LCM modeler show that if the current trend continues in the region, the development of sand dunes and increased rangeland destruction will continue. In the 2030 horizon, sandy zones will cover about 17 percent of the area, and the extent of the desert and poor rangelands will be about 60 percent.

River and river processes are considered as the most significant geomorphic systems which are active on the earth’s surface (Bag, 2019). Over time, many changes in the morphology and dynamics of the river system can occur. The effects of river adjustment caused by the natural factors require much longer time span to be revealed. However, there are few exceptions that the natural factors such as river floods, landslide or earthquake can induce channel adjustments in a very short time (Chaiwongsaen et al., 2019). On the contrary, human activities can have a significant and rapid impact on natural processes and trends, resulting in a compressed time scale for river adjustments (Rinaldi & Simon, 1998). Morphological responses may include subtle shifts in cross-sectional stream channel geometry or widespread landscape transitions, involving progressive or abrupt change over daily to millennial timescales. In order to sustainably manage river systems, it is necessary to further investigate the characteristics of variation in river morphology at various temporal and spatial scales (Minh Hai, 2019). Givi Chay River is one of the permanent rivers of Ardebil province in northwest of Iran and there is still no comprehensive study on this river. This study attempts to investigate the changes of morphological of the Givi Chay River over the time period 2000-2019.2. Study AreaGivi Chay River with almost 54 kilometers is one of the permanent rivers of Ardabil province. Two rivers of Hiro (which is emanated from Khalkhal altitudes) and Arpa chay(which is emanated from north to south), are linked to each other in downstream and the stream around Inalava village is departed toward westward and between altitudes of Khalkhal and Givi reaches to Givi city through a narrow valley. In this area, that river is called Givi Chay. The river flows into Ghezelozan after crossing the city of Givi and joining the Firoozabad River.

In this research, the topography map with a scale of 1:50000, geology map with a scale of 1:100000, and google earth and Landsat Eight images, including OLI sensor (2019), Landsat seven including ETM + sensor (2000), bedrock maps and the Givi Chay River privacy at a scale of 1:2000 hydrological data and field data were used. Moreover, to control the results obtained by quantitative methods it is used from field studies for confirmation and verification. ENVI 5.3, Arc GIS 10.5 and Excel software were also used for image processing and data analysis. The geomorphological parameters of the river and their variations including bending coefficient and central angle were measured. The curvature coefficient is one of the few criteria used in river shape segmentation using s=1/ (y.2), i.e., by dividing the valley length by wavelength for each arc (Pitts coefficient) which was calculated. The central angle of the arcs on each of the intervals was calculated using the relation A=180L/Rπ, where A is the central angle, R, of the fitted circle radius (Kornias coefficient). The lateral changes of the canal were investigated using transect method and calculation of river migration rate. According to the Transect method, lines with distinct distances from both sides of the duct are depicted as baselines. These lines are constant for the time periods studied and hence can be calculated quantitatively for duct movements relative to these lines. When the conduit is moved in the right direction, the area of the right-hand transect of the conduit decreases and is added to the area of the left-hand transect of the conduit, and vice versa. In this study, the Givi Chay River channel was divided into 23 transects based on morphology and channel change trends and quantitative indices were calculated for each transect. The Rm = (A/L)/Y relation was also used to calculate the channel displacement rate. In this respect, RM stands for transverse displacement intensity, A for area between two centerline lines, L is centerline length at time t1 and Y is number of years.

The mean curvature coefficient for the first period in 2000 was 1.48 and decreased to 1.40 in 2019. But in other periods from 2019, the bending coefficient has increased compared to 2000, as the bending coefficient from 1.23 to 1.25 in the second period and from 1.85 to 1.86 in the third period and from 1/15 to 1/18 in the fourth quarter it increased. In general, the lowest bending coefficient for each period is in the fourth interval and in a finite amount. In the first, second, and fourth intervals, most of the intervals in both study periods have a curvature coefficient of 1.5-1.5 and therefore the conduit plan is sinusoidal, but in the third interval more than 60% of the range has a curvature of 1.5 to 2 and therefore the interval pattern is in the form of a Meander. In the second and fourth intervals, the standard deviation of the bending coefficient is low and in the second interval is 0.19 in 2000 and 0.18 in 2019 and in the fourth interval is 0.14 in 2000 and 0.12 in 2019. In general, they indicate the existence of similar arcs. In the first and third intervals, the standard deviation is relatively high for both periods, indicating non-similar arcs.In both periods, the first and the third intervals were highly developed in the form of Meander and the fourth period were of the developed Meander type. However, during the second period during the period, the type of the rift from the developed meander changed to the highly developed meander and the central angle reached from 143.82 in 2000 to 163/50 in 2019. Due to the low valley bed and its alluvial slope, which is associated with complex mazes, and with increasing spiral arc and river energy concentration, the intensity of erosion reaches its maximum and where it is a maze arch, it is concentrated on both sides. A large amount of excipients flows into the bed. As the spiral energy intensifies, the width of the floodplain increases due to erosion. In the first interval, the central angle of the riverbed has decreased in 2019 compared to 2000, and with the decrease of the central angle of the river, the mean radius of tangent to the riverbed has also decreased and in other intervals has witnessed an increasing trend of the central angle during the study period. Increasing the central angle indicates that the river meanders are active and the morphology of the river has changed to a highly developed rudimentary twist, as well as a change in the central angle in bends that have not been removed and only changes have been made in it. In the third interval, the mean central angle in both periods is higher than other intervals. In fact, the river flows in a winding direction due to the geological resistance of the river and the low width resulting from this factor.The maximum amount of lateral changes in transect 12 was 1.51 m and in this transect during the study period 5.47 hectares decreased from left bank and added to right bank. The lowest location in Transect 20 is 0.54 meters, thus reducing the left bank to 1.13 hectares and adding to the right bank of the river. Transverse displacements have often occurred in parts of the river course where the riverbed has floodplains, and the riverbed in these areas is significantly wider and the slope greatly reduced and widened significantly. Agricultural lands and gardens are visible in these areas.

According to the results of the calculation of morphological indices, the average bending coefficient in the first period decreased in 2019 compared to 2000, but the coefficient of bending increased in the 2nd, 3rd and 4th intervals. The first, second, and fourth intervals have sinusoidal plans in both study periods, but in the third intervals in both periods, the interval pattern is a meander. In terms of the central angle index, in the second interval during the study period, the type of interval changed from extended to highly develop. However, the first and third intervals, in both study periods, are highly developed in the form of a meander and the fourth period are in the form of a developed meander. In the plain, the main factor affecting the river meandering is the alluvial formation, the slope is low and the meanders are inscribed and plain, whereas in the mountainous part the river changes are subject to valley changes and the meandering state is seen throughout the valley.The results of lateral conduit changes also showed that the average migration rate of the Givi Chay duct during the 19-year interval was about 0.87 m/year. It should also be noted that, during the period 2000 to 2019, approximately 39.52 hectares were generally added to the right bank of the river and 11.62 hectares decreased to the right bank.In general, changes in the Givi Chay River Plans have been attributed to the expansion of existing meanders, displacement of the river path, and increased curvature and formation of small meanders. Hydrological processes are caused by the process of supply of sediment and sediment discharge, dam construction and lithological resistance of the riverbed as well as human interference such as, encroachment of the riverbed, construction of bridges, sand harvesting.

Metropolitan areas are exposed to natural hazards for various reasons. These risks, which carry numerous life and financial damages, require urgent prevention and action. Flood is one of the major risks in the world and Iran. Tehran, as the capital of Iran and one of the largest population centers, is of great economic, social and political importance in its development, security and urban sustainability. Because of the risk factors and limitations arising from natural and geographical factors within the metropolis of Tehran, increasing human interference in the natural environment and leading to the occupation of hillsides and mountains are encroachment. In this descriptive-analytical study, flood hazard in region 1 of Tehran metropolitan ​​ was assessed.2. Study AreaRegion One of Tehran Municipality, with an area of ​​3604 hectares, is the northernmost district of Tehran, so that its northern border coincides with the northern border of Tehran (1800 meters high lines). This area is bordered on the west by Darkeh floodway- valley with region 2, on the south by Chamran, Modarres and Sadr highways with region 3 and on the southeast by Azgol highway with region 4 of Tehran municipality. The main land use in the study area is a residential and due to the presence of extra-regional and extra-urban uses, especially diplomatic and tourism uses is special importance and its population is constantly growing. This area has natural features such as rivers, valleys, hills and mountains which in various ways has caused lack of development and safety or in some cases led to development.

Flood hazard zoning is one of the spatial analyzes that has great impact on reducing the costs of setting up and running different activities and is therefore one of the most important and effective steps in executing projects. In order to implement a successful zoning, it is necessary to study all the effective factors at the level of the study area and provide appropriate zones in the form of output of the zoning process to managers and final decision makers so that these people are based on existing policies and priorities select the appropriate options for each result. In this research, the study and identification of factors affecting flood risk zones have been considered and ARC GIS as a powerful instrument in the management and analysis of spatial data is used. Then, while selecting and identifying appropriate criteria for flood risk assessment in region one of Tehran metropolitan for urban development and safety, a combination of Analytic hierarchy process (AHP) and fuzzy logic models was used.
The steps of these two models are as follows:1- Determining the criteria and sub-criteria for flood risk zoning in zone;
2. Evaluating the criteria and sub-criteria;
3- Creating Information Layers for Sub-Criteria;
4- Optimizing (standardizing or fuzzing) information layers based on fuzzy logic;
5- Applying the final weight to the criteria and mixing with the fuzzy layers;
6- Final zoning of flood risk in Tehran metropolitan area.
According to studies and studies on flooding, many factors are involved in flooding. According to experts in this area, and considering the characteristics of a Tehran municipality area, the most important factors that contribute to flooding in this area include land use, slope, elevation, distance from drainage network and geology (lithology). The final weight of each of the effective elements in flood risk zoning in a Tehran city was identified by using of Analytic hierarchy process (AHP) using pairwise comparisons and its analysis by Expert Choice software.

Tehran as a metropolis and capital of Iran, with a population of about 12 million, exposed to high altitudes and the presence of numerous rivers and valleys (Kan, Farahzad, Darakeh, Darband, Valenak, Golabad and Darabad), and relatively high rainfall, and Human Interventions (Invasion and invasion of Rivers ents. Increased asphalt and impermeable levels of the city, converting natural basins into integrated basins, affecting and prone to the occurrence of floods (previous flood events). Including the flood of 1987 Golobradeh and Tajrish and flooding of Tehran subway due to the late April 2012, in case of such danger, causing huge financial damage, flooding the streets and passages, creating traffic can be a national catastrophe, leading to chaos and weakening security and development in Tehran's metropolitan area and consequently the area. To help the welfare and security of citizens and to prevent insecurity and chaos after such a risk requires the use of a systemic (basin-based) approach and a distance from the confines of the mosque and the use of various experts, and it is among geomorphologists.As shown in Figure (9), the flood risk zoning map shows that the high- and high-risk areas that cover 50% of the study area are most consistent with the urban area of ​​Area One and the corresponding regions. Drainage networks and outlets are the dominant catchment areas of Area One. But the lower risk areas are more in line with the northern and central parts of the range and areas of vegetation, and generally, the higher we move upstream and north of the basins downstream and south of the catchments, the greater the risk of flooding.

Metropolis of Tehran is the capital and largest core demographic of our countries, have a more importance of economic, political and social, that development, safety and stability urban of its need to more attention. Research Method is analytical - descriptive and library studies and field studies using GIS software and Expert Choice with (AHP-FUZZY) combined model led to mapping zonation of flood risk in the Region1 of Tehran metropolis is prepared. The aim of this study is identifying risk flood zones and its impact on urban safety of Region1 of Tehran metropolitan. The results show that risk map zones of low, low and moderate in the north and center of the study area and much and very much risk zones, of which includes about 50 percent are located in the area, adopts the output of basin and metropolitan area. With consideration of final flood risk map zonation in order to development and safety urban should be prevent of constructions in much and very much areas of flood risk and streams margin and several stream-valleys in Region1 of Tehran.

One of the major problems facing most of the world's major metropolises and cities is natural hazards. In seismic countries, one of the most catastrophic is the earthquake event. Using statistical and probabilistic methods called seismic hazard analysis it is possible to ensure the safety of structures against earthquakes. Every year, a lot of researches have been done to determine the hazard of earthquakes around the world; Therefore, it is necessary to use new and up-to-date methods based on which seismic hazard maps can be updated in Iran. In this study, using a probabilistic approach and risk-targeted hazard analysis approach according to ASCE 07-10, the seismicity of Siraf port in Bushehr province was investigated.

In the present study, in order to investigate the seismic status of the site, a set of historical and instrumental seismic data with a time coverage up to 2019 up to a radius of 150 km was used and seismic sources were modeled. For this purpose, seismic sources in the desired area, using geological maps and satellite images, were determined and a suitable model of seismic sources in the region was presented.The list of earthquakes that occurred in the project area was made through documents, books, and Accelerometer. The defect in the catalog were eliminated by using the Kijko -Sellevoll method. In order to achieve the Poisson distribution of events, foreshocks and aftershocks were eliminated using Gardner and Knopo methods and Grünthal method. Finally, seismicity parameters were calculated based on data analysis in EZ-FRISK 7.43 (2010). The results of seismic hazard analysis using the probabilistic method for Siraf port are presented as a risk-based at the design level for a return period of 2475 years.3. Study AreaSiraf port is located in Bushehr province near Kangan city in the south of Iran, between the south and southwest of the country (27.6667 °N, 52.3425 °E). It is one of the oldest ports in Iran, which is located between Kangan port and Assaluyeh port.The geological structure of the site follows the general trend of the Zagros with a northwest-southeast trend. The location of the site from a geomorphological point of view has a flat and plain topography and from the point of view of structural zones of Iran, it is located in the folded Zagros (Darvishzadeh, 1991).

4.1. Determining the maximum horizontal acceleration of the ground motionUsing the results of the probabilistic method, the parameters of the maximum acceleration values of the horizontal component of the site at the seismic levels of operation basis level (OBL), design basis level (DBL) and maximum consider Earthquake (MCE) are 0.11g, 0.39g and 0.61g, respectively.4.2. Risk-Targeted response spectrum for design levelThe design response spectrum based on risk concept (ASCE 07-10) for this site has higher values in the entire period interval (approximately 12%) than the design spectrum with a probability of exceedance 10% and 2% in 50 years.The increase in the values of the risk-based spectrum in the site compared to the uniform hazard spectrum is due to uncertainty in the collapse capacity of the designed structures. Therefore, the probability of collapse and failure of structures designed according to this spectrum by changing from one place to another and by changing the shape of the seismic hazard curve, leads to the probability of non-uniform collapse.According to the Hazard Zoning Map of Iran in Regulation 2800, the location of the site confirms the level of high seismic hazard and the amount of acceleration of the design is 0.3gacceleration. While the earthquake hazard analysis of this region with a probability of exceedance 2% and 10% in 50 years has determined the parameters of maximum horizontal acceleration 0.61g and 0.39g, respectively.The spectrum estimated in Standard 2800 is based on 10 percent probability of exceedance within a 50-year period with a Return period of 475 years. In seismically active areas where earthquakes occur most frequently, such as the west, southwest, and south coasts of the country, this method may be a logical one. But in areas where earthquakes are less common or the sensitivity of the area or site is important, the prediction of an earthquake with a return period of 475 years is under-predicted; Therefore, the definition of a maximum considered earthquake with a 2 percent probability of exceedance within a 50-year period with a Return period of 2475 years should be reconsidered.Finally it is worth mentioning that the estimated probability of collapse in 50 years for a structure designed for the probability of exceedance 2% in 50-years, with the 2/3 factor, is indeed more geographically uniform than that designed for the probability of exceedance 10% in 50-years ground motions, without any factor.

Due to the high seismicity of Iran and especially the high importance of the southern regions of the country, it is recommended that the spectrum of regulations of the country (including standard 2800) be extensively studied and updated. Therefore, it is recommended to modify the spectrum in these regulations by updating the design spectrums of these regulations, using the methods available in valid standards such as ASCE7, in which earthquake estimation has been done properly. And for very important areas (including Tehran, Bushehr and Tabriz) due to the hazard of earthquake and irreparable damage, it is recommended to use the design spectrum based on the concept of risk-targeted according to ASCE 7-10.The results of the study indicate that the seismic values of the spectrum obtained according to Regulation ASCE 07-10 are different from the proposed values for this area in the 2800 standard. The reason for this is the uncertainty in the seismic design of the structure that the risk-targeted approach is able to take into account and leads to achieving a uniform level of geographical distribution to prevent the collapse of the structure.

UHI (Urban Heat Island) describes the phenomenon of temperature change in urban areas than their surroundings such as bare lands, gardens. Its effects are: increasing energy and water consuming, escalation of air pollution and intrusion on thermal welfare (Hashemi et al., 2019). Different researches about UHI, have assessed the effect of one or several factors, mainly land use, on the escalation of surface and sensible temperature. There are many researches about this issue. In one case in Macedonia, Skopje, heat island was assessed by using of NDVI index. The results indicated NDVI index was effective in weakening heat island and NDBI index had positive correlation with surface temperature which shows manmade areas have effects on heat island intensification (Kaplan et al., 2018). RS (remote sensing) makes it possible to assess all aspects of UHI as a hazard through preparing high quality satellite images. GIS (geographical information system) does too by preparing database, uniform methods, analysis and producing maps. The objective of this paper is temporal-spatial analysis of heat island of this hazard to recognize the UHI places in relation to land uses. This analysis helps urban managers to know more about spatial requirements in city and the importance of proper places (green lands and parks) to moderate urban temperature.2. Study Area Urmia city is the central district of Urmia county in the center of west Azerbaijan province in Iran. The city has been located in the distance of 18km from Urmia Lake. The city is located in 37 ºN latitude and 45 ºE longitude. Its climatehas beenaffected by latitude, winds, Urmia Lake, Mediterranean wet climate, Siberian cold air masses, topography, altitude, and the direction of altitudes (Javan, 2013).

In this study Landsat images for August period of 1989, 1998, 2011 and 2018 were used. Preprocessing, atmospheric and radiometric correction were performed for the images. To prepare heat island maps, land surface temperature (LST) was calculated by the threshold limit of NDVI and Plank principle method for TM images and Split Window algorithm for TIRS image. The algorithms were run in ENVI 5/3. LST formula factors for TM images included Brightness Temperature and Land Surface Emissivity (LSE). To calculate LST for TIRS bands, Split Window algorithm was run by using some important factors including brightness temperature, split window coefficient values, mean LSE, difference in LSE and atmospheric water vapor content. Using of NDVI and land-use images, the relationship of LST, vegetation cover and different land uses was analyzed. For assessment of heat island intensity changes in Urmia, the index of heat island proportion was used for the images. See the following: temperature of place       Average surface temperature

According to land use maps, during 29 years from 1989 to 2018, Urmia city has had significant growth and expansion. Change detection results indicated  that about 82%, equal to 2475 hectare of bare lands and 72%, equal to 1833 hectare of surrounding farmlands and gardens of the city have been changed to urban land use and related constructions. Using Zonal statistics in Arc GIS the temperature of land uses was assessed .According to the results in 1989 the green cover had the lowest temperature, and the bare lands had the highest average temperature. In August 2018, the maximum temperature is related to urban areas. On August 2018 some new UHIs have been made which are related to producing and industrial workshops, buildings and bare lands in the north east, east and south east of city. The assessment of UHIs changes process showed the escalation of UHI index in Urmia.

Urban Heat Islands have destructive effects for metropolises and the residents. Urmia as a metropolis has had rapid industrial and population growth in the recent decade. In this research assessments have been done by Landsat images from 1989 to 2018 with four years with temporal distances. The results indicated that in 2018 the area of cold and very cold temperature classes have been decreased. It is because of destroying of large areas of farmlands and gardens and changing to urban land use during studied years. In 1989 and 1998 very high temperature class included bare lands. Passing years and constructing new buildings, industrial and production workshops, new UHIs have been created in east, north east and south east in 2018. The results indicated that during the studied period, according to UHI index results, UHI has been intensified. The UHI index with the amount of 0/2 in 1989, has been reached to 0/37 in 2018. According to these results, in addition to extending of UHI in Urmia, UHI has been intensified. Another research which has been done on UHI in Urmia in July 2015 (Asadi et al., 2019) had the same results of this paper. It proved the effect of industrial and administrative land uses on high temperatures. This research represented the negative relation between LST and green lands and vegetables. Maleki et al. (2018) using synoptic stations information and statistics to assess the UHIs in Urmia and recognized the UHIs in same places like this article. This shows that satellite images have high accuracy and efficiency in analyzing natural or manmade phenomena.

Drought is one of the inseparable phenomena of climate fluctuation. Reduction of rainfall and fluctuation of other climatic parameters affect the types of drought. Drought indices have been developed to monitor the drought situation and to evaluate its quantitative effects. The most widely used variable in meteorological drought monitoring was the amount of precipitation, which was the only determining parameter in the initial drought indices. In the recent years, indices based on precipitation and evapotranspiration were also developed.One useful method of predicting drought is using a Markov chain. Vulnerability, reliability and resiliency are very common criteria in evaluating the performance of water resources systems that have been used in various studies on drought. The total probability of droughts based on the drought indices is the same as the vulnerability. Resiliency indicates the probability that the system will return to normal condition after a period of drought. Reliability means the probability that a drought will not occur within a certain period of time.According to the mentioned issues, the main purpose of this study was to investigate the characteristics of drought including vulnerability, reliability, resiliency and persistence in three wet, normal and drought conditions within eighteen synoptic stations of the country (Iran). These stations are located in different geographical and climatic locations. These characteristics will be examined based on four drought indices including SPI, SPEI, RDI and eRDI, in the scales of water year and plant growth periods.2. Study Arearan is located in a arid and semi-arid zone of the world with an average annual precipitation of about 250 mm. Iran is classified into six climatic zones including coastal wetland, mountainous, semi-mountainous, semi-desert, desert and coastal desert. In this study, three stations from each climatic zone and a total of 18 synoptic stations were used. For this purpose, monthly statistics of meteorological variables of precipitation, minimum temperature, maximum temperature, relative humidity, sunny hours and wind speed were used for 18 synoptic stations. The statistical period of the studied stations is from 1957 to 2016 (59 years), except for one station.

The SPI Index is one of the most widely used indices in recent decades to monitor drought around the world. The standardized precipitation-evapotranspiration index (SPEI) is proposed using precipitation and evapotranspiration data. The Reconnaissance Drought Index (RDI) is proposed based on the concepts of the SPI index and the ratio of precipitation to evapotranspiration to monitor drought and take into account climate change. The effective Reconnaissance Drought Index (eRDI) is presented in the RDI index to improve the results of drought assessment, especially agricultural drought.In many hydrological and water resources models, forecasts at one time are influenced by values at other times, which is a Markov chain process. Most researchers have used a one-step change in the Markov chain to predict drought. The probability of occurrence of the phenomenon is conditional on the occurrence of a particular phenomenon at a previous time itself. In the present study, the number of rows and columns of the matrices is proportional to the three humid or dry status classes including drought, normal and wet conditions. Then, the statistical criteria of drought characteristics of durability, vulnerability, resiliency and reliability were determined through the values of matrices, according to the definitions provided in the introduction.

Results show that the probability of resiliency on an annual scale, according to all indices for all stations has an average of 0.83, which indicates that the average probability of drought in two consecutive years is about 0.17. The results of the reliability characteristic on an annual scale also show that according to all indices for all stations, its value varies between 0.78 to 0.92. In terms of vulnerability, on an annual scale in all stations and indices, the probability of vulnerability varies between 0.08 to 0.22. In general, the SPEI index shows higher values of drought vulnerability than other indices in this time scale. The resiliency in the growth period in all stations and indices varies between 0.46 to 1. Reliability also varies between 0.75 to 0.92. In this time scale, the eRDI index shows higher values of reliability and lower values of vulnerability than the RDI index in all stations. The same is true for the annual time scale. In terms of vulnerability, this characteristic varies between 0.08 to 0.25.The probability of the normal condition persistence on an annual scale is between 0.4 to 0.9 and its average is 0.67, for all stations and the desired indices. On average, the eRDI index shows higher values of normal status, and among the climates, the humid coastal climate shows the lowest probability of persistence. The probability of remaining in the normal conditions in the time scale of the growth period in average is 0.67. Among the climates, the lowest and highest values are related to the humid coastal and desert climates, respectively. Also, the probability of remaining in drought conditions in this time scale in average is 0.16. Among the climates, the lowest probability of persistence in this situation is related to the coastal desert climate according to all indices except the SPEI index.

Markov chain model and transition probability matrix can be good tools for drought monitoring and forecasting. In general, the SPEI index shows higher values of drought vulnerability than other drought indices. Hypersensitivity of SPEI index to evapotranspiration has caused this index to show more variability in drought conditions. Results of this index show that, compared to other indices, in almost all stations there is less reliability but more vulnerability to drought. Based on the results of the transition probability matrix, it can be stated that in most cases the values of the original diameter of each matrix are larger than the values of the other elements of the matrix, which indicates that the probability of persistence of that condition is higher than other states. In addition, the probability of normal condition persistence is much higher (more than 50%) compared to both wet and drought conditions. According to the obtained results, it is suggested that in the analysis of drought characteristics, their characteristics such as vulnerability, resiliency and reliability should be examined according to the type of climate.

Natural hazards, especially earthquakes, often result in heavy corporal and financial losses to human settlements. Therefore, all communities are vulnerable to natural disasters, which can change the quality of life of communities; Iran has always suffered a lot from natural disasters due to its spatial structures and has been one of the most vulnerable parts of the world in terms of environmental hazards, including earthquakes. Rural textures of residential spaces in the nature bed are highly vulnerable for the following reasons: inappropriate infrastructure and existing socioeconomic inequalities, low housing quality, low level of use of technology, non-compliance with location criteria, etc. These face a high level of vulnerability.Nowadays, considering the vulnerability of villages in different spatial dimensions, the resettlement policy in the form of three redevelopment (reconstruction), relocation and aggregation approaches, is one of the types of management approaches in rural settlement planning which in order to develop rural districts and specifically to organize the optimal distribution of rural areas and the provision of facilities and services for villagers and, ultimately, the protection of villagers against all kinds of hazards have been proposed. These policies focus on two issues of welfare and quality of life. In fact, one of the new approaches to resettlement is the introduction of indicators of quality of life.In this study, evaluating the effect of resettlement policies after the earthquake on the improvement of economic, socio-cultural and spatial-physical quality of life of villagers was investigated using 11 indicators. The purpose of this study was to investigate the effects of relocation, integration and aggregation of villages damaged by the 1997 earthquake in Zirkooh county on the quality of life of local residents through studying mental indices. The research question is: How have post-earthquake enforcement policies (integration, aggregation, relocation) affected life quality of villagers?2. Study AreaZirkuh county is located in the northeast of South Khorasan province and its center is Hajiabad city. This county is bordered by Khorasan Razavi province to the north, Darmian county to the south, Ghaen county to the west, and Afghanistan to theeast. Zirkuh city includes three districts, six villages, and two cities named Zahan and Hajiabad. The total number of settlements in this city is 138, of which 103 are inhabited and 35 are uninhabited (Deputy Planning of South Khorasan Governorate, 2014). The study area is the villages affected by the earthquake in May 1997. Among them, six villages of Pardan, Payhan, Afin, Mehmanshahr, Ardakol, Darj Olya in which three resettlement strategies (consolidation, relocation and relocation or reconstruction) have been implemented, were selected as a sample.

The method of research according to the nature of work is descriptive-analytic. In the first section, to determine the appropriate criteria by studying library documents, theories about quality of life and resettlement were examined. Then, by merging them, the criteria for assessing the quality of life of relocated or integrated rural settlements were detremined. In the second part, using the observation tools and questionnaire, the required data were collected. The population of the research of the villagers affected by the earthquake in 1976 in Zirkuh district in six sample villages is 903 households. According to Cochran's formula (95% confidence level and 0.05 error), 144 households were randomly selected to complete the questionnaire. The data were collected and analyzed. The questionnaires were basically closed questions with answers in five-point Likert scale (very (5) to very low (1)). Reliability of the questionnaire was confirmed by Cronbach's alpha method, and alpha value of 0.855. Data analysis was done in two sections: descriptive and inferential statistics, by SPSS software and Kolmogorov-Smirnov tests, single sample T and analysis of variance (ANOVA).

In this study, the effects of resettlement strategy on the quality of life of residents of displaced villages were studied. Comparison of three patterns of reconstruction, displacement and aggregation of villages in the study area showed that each of the studied patterns has advantages and disadvantages and none of the patterns was able to improve the quality of life of the studied communities in all areas. The results of respondents' survey on the quality of life dimensions in the sample villages showed that in the socio-cultural dimension with the mean of 2.717, is the highest and the economic dimension with an average of 2.15 is the lowest satisfaction level that exists. The quality of life is also moderate with an average of 2.576.The results of one-sample T test confirmed the above results. The results showed that one of important indicators in increasing the quality of life of the rural community is social solidarity. In the social cohesion index, the value of the statistic is 16.14 and the level of significance is equal to 0.000 which is less than 0.05. It should be noted that the value of T statistics on the quality indices of infrastructure, health and safety, participation and housing was also recognized important by the villagers.Moreover, results showed that the Mihmanshahr village with an average of 2.69 had the highest and the Payhan village with an average of 2.47 had the lowest level of quality of life, which proves that coherent policy is better than the policy of displacement at the level of sample villages. The results of analysis of variance show that there is no significant difference between the quality of life of a resettlement pattern in the sample villages but significant level in 7 indicators of quality of employment, income quality, quality of education, participation quality, quality of social cohesion, quality of infrastructure and ambient quality is less than 0.05. Thus, at the 95% confidence level, there is a significant difference between the three resettlement policies in terms of quality of life.  Respondents' level of satisfaction with quality of life is not the same in all three resettlement patterns, but in other indicators, there is no significant difference between respondents' satisfaction with quality of life.

It can be concluded that resettlement policies should not only be considered as financial compensation or the provision of means of life. But it should also cover all aspects of life (financial, occupational, educational, social, cultural, environmental and physical, etc.) to reduce the hardship of individuals during the process of implementing the plan. It should be noted that regardless of the choice of any model for resettlement, it is most important to pay attention to the living conditions in the new place to enjoy the working and living conditions of people in a livable settlement and planners should provide a good quality of life in the new settlement - with any pattern of resettlement. Training villagers to have skilled and technical jobs, especially the youth, creating job diversity and creating low-cost job insurance for the villagers to increase job security coefficient are among the suggestions that can be offered to improve this area in the study area.

The need to energy for doing economic activities, and meeting the growing population demand have increased in the recent years. Energy consumption is a prerequisite for economic progress in societies, and its increasing consumption has led to environmental problems, the most important of which is air pollution emissions resulting  from fossil fuel combustion. Due to the wide range of effects of air pollution on local to planetary scales, identifying the factors affecting pollution like carbon dioxide and determining the share of each can be a guide for environmental management in any country. Therefore, this study seeks to investigate and analyze the share of factors affecting the emissions of air pollution in Iran. The innovation of this study is emphasizing on the effect of urbanization along with population growth on air pollution. There are many theories about the effect of urbanization on the environment. Some believe that urbanization is an important factor in increasing environmental pollution and climate change. Others believe that urbanization is a factor in improving the quality of the environment due to high efficiency in energy consumption, and can lead to air pollution reduction. In this study, the amount of CO2 emissions is considered as an indicator of air pollution, and the impacts of factors including population change, urbanization growth and energy intensity on CO2 emissions changes in Iran is analyzed during the period 1997 -2016.2. Study AreaIn Iran, per capita carbon dioxide emissions in 2014 was equal to 8.4 metric tons, and its emission growth during 1990-2014 was about 127 percent, i.e., more than doubled (World Bank, 2014). Energy consumption and energy intensity in Iran have decreased in some years due to the relatively low increase in energy consumption compared to the urban population and have increased in some other years. Considering increasing energy consumption, carbon dioxide emissions have increased by the whole economy.
3.

The first method (IPAT) for analyzing the main factors of environmental degradation is determined by the destructive effect on the environment (I), multiplied by population (P), economic prosperity in terms of the level of production or consumption (A), and technology as the environmental effect of economic activity (T). Some studies replaced the concept of IPAT with IMPACT. In IMPACT model, technology is divided into two parts; the term technology (T) and another term in the sense of energy relative to GDP (C). In this study, complete decomposition method was used to analyze the factors affecting the emission changes. Four factors have been selected in this study: the pollution coefficient effect, energy intensity effect, population structure (urbanization) effect, and population effect. The pollution coefficient effect is determined by the rate of carbon dioxide emission and energy consumption, which is called the carbon dioxide emission intensity. This variable evaluates fuel quality, fuel change (fuel replacement), and the installation of pollution reduction technologies. The energy intensity of urban household effect is determined by the rate of energy consumption and urban population. The energy consumption is mostly related to some variables such as economic and urban structure, transportation efficiency and energy systems of the city, energy use technologies, energy prices, energy saving policy and investment to reduce energy consumption. Urbanization effect is determined by the ratio of urbanization to population. This coefficient measures the relative position of the urban and rural population in an economy and changes with the evolution of urban structure. The population effect is expressed in the size of the total population in an economy.

The results show that the effect of carbon intensity is the most important factor influencing on the increase of carbon emissions. This shows that fossil fuels are widely used, and substitution for clean fuels and pollution reduction technologies in the economy are low. During the considered period, the energy consumption of the urban population has decreased. Decreases in the energy intensity can be due to increased energy efficiency, acceptance of new manufacturing technologies, reduced use of fossil fuels, or changes in fossil energy prices. Despite the declining energy consumption of the urban population, there is still great potential for reducing energy intensity due to the gap in the production process, technology, and level of management.In the whole period of 1997 to 2016, both population and urbanization changes have increased the emissions of carbon dioxide. Urbanization has contributed 52% to carbon emissions change over the entire period. Although urbanization has had a declining effect on carbon emissions between 2007 and 2011, it has had a positive effect throughout the whole period. So, it can be said that the expansion of urbanization in Iran is a factor in increasing air pollution. In previous studies done on Iran, less attention has been paid to the effect of these two variables, and more attention has been paid to the effect of economic growth and the structure of society's products.

Considering the rapid growth of industrial activities and urbanization, the consumption of different types of energy plays an important role in influencing the local environment and changing the global climate. Increasing environmental degradation at the local, national and global levels has raised policymakers' concerns about the side effects of energy consumption and related social welfare. Recognizing the important factors influencing on pollution emissions, and determining the share of each can help in better dealing with this environmental problem. Therefore, the purpose of this study was to investigate the factors affecting changes in carbon dioxide emissions with emphasis on two important factors of demographic change and urbanization in Iran. For data analysis, the computational model of decomposition analysis has been used. The results show that both population and urbanization have played an important role in increasing energy consumption and carbon dioxide emissions. According to the results, strategies such as reducing pollution by changing fuel and switching to cleaner energy, implementing energy optimization plans and upgrading equipment technology, guiding household consumption toward improving fuel consumption patterns, and enforcing pricing and incentive policies should be considered for carbon decrease and sustainable development.

Climate change has become a global issue today, an issue that many people around the world are at risk of severely changing. These changes have led governments to put the preparation of their communities at risk at the forefront of their actions and programs. In this context, the role of governments at the local and national level and minimization programs of the impacts on communities and their associated consequences are important. In order to reduce the direct and indirect vulnerabilities of drought-affected areas, including social, economic, and environmental, and in broader geographical areas, these areas need strategies to mitigate the effects of drought and boost coping capacities. Focusing on adaptability to current climate changes is the most viable response in the face of an uncertain climate future. Since drought is a multifaceted phenomenon in terms of its impact on farmer driven communities, various social, economic and environmental aspects have to be considered to assess the capacities needed to counter this rough phenomenon.Khorasan Razavi is one of the provinces of Iran experiencing a very high drought with amount of 7.89%. On average, 3.7% of the area of Khorasan Razavi province has severe drought and 7.17% suffers from very severe drought. Five province counties have the severe drought more than 50 percent. According to the data from the Meteorological Organization of Iran, until June 2019, Fariman is one of the regions with intense drought conditions in the province with a severe drought of %1.40, a very severe drought of %2.50, and a total of 3.90% of drought. The purpose of the present study was to investigate the perceptions of rural farmer households of Fariman County against drought and long-term strategies for dealing with it to reduce the resulting damages.2. Study AreaAccording to the latest divisions of the country, Fairman is one of the cities of Khorasan Razavi province, which is located in the north of the province. This county located at 58 degrees and 31 minutes to 60 degrees and 34 minutes east longitude and 35 degrees and 28 minutes to 36 degrees and 1 minute north latitude and has an area of ​​about 3225 square kilometers which is equivalent to 1.3% of the total area of the province. The center of this city is Fariman, which is located 75 km southeast of Mashhad along the Mashhad-Torbat-Jam and Herat routes, which is 216 km from the Afghan border. The city is also located at the altitude of 1411 meters above sea level and is 26 square kilometers. At present, this city includes two at the present time, the city consists of two central and non-central parts.

To do this research, based on Lucas and Hilderink's (2005) triple approach, which includes knowledge, ability, and action, we assess the dealing capacities and adaptability in rural communities. Then, two approaches, crisis management knowledge (such as dimensions of prevention, preparedness, exposure and institutional-communication) and local community resilience approach (including structural and non-structural approach), were combined with Lucas and Hilderink's triple approach and examined based on an integrated model The present study, with a descriptive-analytical approach and practical nature, seeks to evaluate and measure the coping capacity of farmers in Fariman, which includes two basic steps:. Determination of indicators and criteria for the dealing capacity and adaptability among rural households using desk studies; 2) 2) Field studies were conducted through survey method and questionnaire tools. The main theoretical approach of the research is the theory of Lucas and Hilderink (2005) on promoting the dealing capacity against drought hazard which includes three dimensions of awareness, ability, and action.

Findings at levels of dealing capacity and adaptability indicate undesirable and weak conditions of farmers against drought. According to the results of the study, farmers have known lack of precipitation with amount of 3.61% of the drought, the According to the results, farmers have considered the lack of precipitation with amount of 3.61% as the main cause of drought in their village, which is consistent with the experience of drought in Iran, especially in the last two decades. Their village, which this issue is consistent with the experience of drought in Iran, especially in the last two decades. The goal of dealing and adaptability strategies is to increase farmers' ability to cope with drought and its effects in different areas. Therefore, assessing ability against drought is another step that points to assessing the potentials of farmers for dealing drought hazard. As the findings of the study show, the average resilience dimension in the prevention dimension was estimated to be 2.27 and it suggests that in the absence of sufficient awareness, the indigenous people will not be able to take preventive measures at the individual or broader level to cope the drought and As the research findings show, the average ability in the prevention dimension was estimated to be 2.27, which indicates that in the absence of awareness, consequently cognitive to predict risks such as drought is not formed or is very weak. This factor makes the indigenous person unable to take preventive measures at the individual or wider level in the face of drought even in structural dimensions of action capacity, the average was reported to be 2.18, indicating a very low capacity of farmers against the consequences of drought in their rural area. In other two dimensions of ability, namely, preparedness and exposure, the mean level was 2.19 and 2.16, respectively. These average levels explicitly These levels explicitly indicate the low level of farmers' ability in the context of coping capacity and adaptability.below the level of ability of farmers in terms of dealing capacity and adaptability. When the low ability capacity is perceived, it does not necessarily lead to action capacities against drought. In the non-structural and institutional-communication dimensions, the findings indicate a worse situation. This means that the local government does not receive significant performance from farmers in discussing the institutional-communication element for drought management and minimizing its effects. This low institutional performance points to the incompatibility between local people and local government, which will be an additional factor in the wider drought in the region.

Overall, the findings suggest that short-term coping capacity and long-term adaptability by farmers are at serious risk. Findings show that at all three levels of awareness, ability, and especially action, the situation is undesirable, and if no effective action is taken either to increase the resilience of farmers or to manage the drought crisis in rural areas, this situation, in the long run may lead to complete soil degradation, loss of groundwater resources, and even complete evacuation or removal of the settlement. Therefore, in this regard, it is better for the local government, with the help of the central government and considering entering the wet season, not only to improve and repair the resources and infrastructure lost during the drought, but also with the adaptive capacity approach to further development the sustainability among local people and at the level of rural development and provide the necessary infrastructure.

Urban development and growth has become an uncontrollable process in most countries across the world, as it can be claimed that more than half the world’s population live in cities now. Urbanization has widely influenced the environment in local, regional, and global scale. Growing urbanization has caused some problems like landfill. Moreover, many cities in Asian developing countries face serious problems in landfill management. Thus, by growing number of people in developing countries, landfill management has become one of the major issues today. Finding an appropriate site for landfill makes an important part of the planning process. The growing rate of population and development of industrial and commercial activities and services have led to the production of vast amounts of waste in cities. Golestan province was separated from Mazandaran in 1997 to form a new province with the center of Gorgan. Since then, its population started to grow and landfill turned to be a major challenge among other things.2. Literature reviewMirabadi and Husseinabadi (2017) studied Landfill Site Selection in Bukan using Analytical Hierarchy Process (AHP) and concluded that the regions between Bokan and Simineh in the southern part of Kani Shaqaq village is the best place for landfill. Ziarri et al. (2013) studied the best location of landfill using Analytical Hierarchy Process (AHP) in Jolfa city and concluded northwestern part of the city is the best place to landfill.Celiker and Yildiz (2019), evaluated the site selection of solid waste landfill using multi-criteria decision analysis and geographic information systems in the Elazığ city, Turkey. The results revealed that the landfill suitability index values for the selected site range between 2.64 and 6.10. The major part of the landfill site has relatively low index values implying that the selected site is suitable for solid waste landfill.Al-Karadaghi et al. (2019) in Sulaymaniyah, Iraq, used multicriteria decision-making methods (WLC) and GIS for landfill site selection, and seven appropriate sites for landfill were suggested. All of these sites adopted the scientific and environmental criteria.

Gorgan is a city located in northern Alborz heights which covers an area of over 10883 Hectares (Jahani Shakib et al., 2018) and it ranks 4th among the cities of Golestan province. In order to find a location for After categorizing the factors,questionnaires were classified from 1 to 9 by experts. Number 1 had the lowest score and number 9 had the highest score. Then, expert choice was used for weighting the indicators in AHP Model. At the end, in Artificial Neural Networks (ANN), each of the indicators was fuzzied first, and then the artificial neural network was implemented.

Results show that according to experts, in AHP Model, slope and geology are top priorities and distance to fault, height, and distance from airport have the lowest priority. In AHP Model, areas in the North-east and parts of Southern area as well as areas located in the middle belt of the city tend to be more appropriate for locating landfill; because  they are far from water wells, faults, villages, the city, airport and the river and the elevation of these areas are suitable for landfilling.The previous site for landfill, located around Hezar Pich in Gorgan, has not been a suitable place according to AHP Model. According to ANN Model, Northern areas of Gorgan are inappropriate for landfill because they are both close to the city, village, airport, and surface water networks and are geologically improper for having young alluvium and alluvial fans. It must be noted that Hezar Pich is not an appropriate site for landfill according to ANN Model as well.

 This study saught to locate the best site for landfill in Gorgan and had a look at previous site of the city as well. This was achieved using 11 criteria and geographical data focusing on AHP and ANN techniques.Results obtained from 2 models AHP and ANN revealed that the most inappropriate sites for landfill were Northern areas of the city due to small distance from underground water wells, airport, cities, villages, asphalt roads and unsuitable geology. While appropriate sites for landfill, according to AHP and ANN, were areas in the North-west, North-east, middle belt of the city, and some Southern parts of the city. It is noteworthy that Hezar Pich area was improper for landfill used in the past. Waste material is currently buried in Western part of the province (Aq Qala). This factory is located 40 Kms away from center of Gorgan, somewhere between two cities of Aq Qala and Gamish Tappe, covering an area of 80 Hectares. These waste materials are transferred to the factory to be recycled and processed and finally converted to organic compost.

For the current age, with respect to the population growth and an increasing need for water, pressure on water resources has turned into a bioenvironmental challenge. This issue is seen more critically in dry and semidry countries such as Iran. In the same vein, flow-regime transitions as an initiative force to stabilize stream/river ecosystem is of significance since as a result of meddling with the natural areas of rivers such as dam construction, some bioenvironmental and geo-environmental adverse effects especially for downstream regions are the result. Therefore, the current study attempts to analyze the effects of the construction of Salma Dam on Harirud in Afghanistan which has raised issues in downstream regions (Iran Harirud) such as desert expansion, exacerbation of wind erosion in border regions of Harirud River, groundwater depletion, resulting in soil humidity decline, increase in soil salinity and so on. This study aimed to introduce the geo-environmental effects of Salma Dam construction and Harirud dehydration in Iran which has ultimately resulted in a decline of bioenvironmental potentials of the region. River dehydration has definitely natural adverse effects and challenges on the frontiersmen lives. 2. Study AreaHarirud in the eastern borders of Iran is viewed as the natural boundary, bordering Iran-Afghanistan and Iran-Turkmenistan. Our target area is about 190 kilometers long and five kilometers wide. The study area includes a zone featuring 61 degrees, 02 minutes and 61 degrees, 15 minutes in East longitude and 34 degrees, 55minutes to 35 degrees, 45 minutes in East latitude. The present study got the data from Harirud Hydrometric Station (Khatoon Bridge), founded in 1967, whose data range from 1977 up to 2016 were used. Before the Salma Dam was constructed, the natural medium flow was around 50 cubic meters. Moreover, the average annual rainfall of the eastern regions of Iran for Harirud basin is 188.7m

The current study selected Harirud river for 190 kilometers long to Taibad and Doosti (Friendship) Dam Reservoir. To this end, ecohydrology models such as Tennant, tessman, Desktop Reserve Model (DRM), and flow duration curve shifting (FDC shifting) were used. In this case, the Tennant Model uses percental average of annual flow in order to determine the quality of fish habitats. This model was firstly used in Montana, Nebraska for 58 cross-sections of 11rivers, in which Wyoming concluded that at least 10 percent of annual average flow (AAF) is essential for fish short-term survival. In 1980, Tessman adapted Tennant Seasonal Model and integrated Mean Monthly Flow (MMF) and Mean Annually Flow (MAF) to determine the minimum amount of adequate monthly flow. Besides, Desktop Reserve Model (DRM) is capable of providing ecological flow requirements once a quick evaluation is needed and the data set is limited. In ecohydrological method, another model is flow duration curve shifting (FDC shifting) which has been proposed by Smakhtin and Anputas (2006), which intends to evaluate bioenvironmental flow in rivers, in order to provide protection in optimal ecological states and includes four major steps. All of these have been used in this study to analyze the river flow necessities of Harirud River, due to water decline as a result of Salma Dam construction.

According to the Power Ministry issues, and based on the Tennant Model, the acceptable levels for the first and latter halves of the year are 30% and 10% MAR, respectively. The logic behind the selection of six-month terms is two high-water and water-scarcity periods. Based on the hydrological data of the region, the two high-water and water-scarcity periods are from February to May and from July to December, respectively. Accordingly, for February to June and for July to December, the amount of bioenvironmental water needed based on Tennant Modified Model was calculated as 14.9 and 5 cubed meter per second, respectively. Bioenvironmental needs were met based on the study range in Tessman Model. According to the cases mentioned in Tessman Model for Harirud River, an average flow of 26.5 cubed meter per second (equal to 53% of Debbie Annual average) is needed. With regard to the ecological evaluation of the river, and based on the definitions in DRM Model, Level C (river changed state) was selected as an optimal ecological state. Since the river goes through two high-water and water-scarcity periods, that is, from march to May and from July to October, respectively, time series for monthly inlet flows were shifted two months (May was shifted to Day etc.). The results from the model showed for river life survival in level C, an average of 10.6 cubed meter per second was needed. GEFC software was used for FDC shifting. Flow duration curve shifting (FDC shifting) and target curves from bioenvironmental layers were studied in the river. With regard to the bioenvironmental importance of Harirud River, level C of biological classification (minimal condition for river survival) was selected as optimal. The statistics were conducted based on Tennant and Tessman hydrological Models and hydrological data in order to meet bioenvironmental needs and the results obtained from these Models are not directly related to the ecological features of the river system. 

In the current study, a vast range of ecological methods were used and attempts were made in order to exert modifications on how to apply the above-mentioned models, so that a near-natural condition could be studied. According to the classifications performed from the statistics, it was found that Harirud River zone is mostly located in level C of biological classification and in proportion to the vast area of Harirud River in Afghanistan and Iran, it holds an alarming condition which is definitely the result of Salma Dam construction and its water reservoir. It merits noting that the methods and the amounts recommended in this study are not the ultimate solutions for bioenvironmental issues of this river, since lack of comprehensive ecological data which is required in the studies of river ecosystem renders lower reliability in ecohydrological evaluations. But it is evident that as a result of a decline in the needs of bioenvironmental flows following water-scarcity, the quality of surface and subsurface water will drastically change leading in the aggravation of biosphere and eastern frontiersmen’s lives. And that is because water degradation of Harirud River and its subsequent adverse results directly exert challenges for the region which is highly subject to and affected by agricultural economy, of which to name the natives’ immigration to the larger cities where adverse consequences are created.

Flooding is a natural phenomenon that human societies have accepted as an inevitable event, but the occurrence, magnitude, and the frequency of the flood are affected by many factors that vary depending on the climatic, natural and geographical conditions of each region. International UN surveys suggest that floods should regarded as one of the most serious natural disasters, and that only a few countries in the world are free from flood and flood issues. Due to the type of rainfall and geomorphological status of catchments in Iran, most of the regions and cities are exposed to floods. Some cities are more at risk because of their location. Poor growth in planning, population density, poor infrastructure, deforestation, etc. are among the factors that increase the likelihood of disaster. Badavard River basin in Lorestan province due to geomorphological and physiographic situation of the basin and having high rainfall (average more than 2 mm/year) has great potential for flooding, Accordingly, one of the cities that are subject to geomorphological hazards, including flood hazards, is Nurabad city in the Nurabad county which has undergone significant physical development in recent years. There are many settlements, especially in the southern areas of the city, and due to the morphology of the area it is anticipated that in recent years the development trend will be towards the southern areas as well as the river margins of the city.2. Study AreaNurabad city is surrounded by Selseleh, Doreh and Kuhdasht cities in Lorestan province, Shirvan and Chardavol in Ilam province, Kermanshah, Harsin, Sahneh and Kangavar in Kermanshah province and Nahavand in Hamadan province. Nurabad city is located between an altitude of about 1000 meters to 3500 meters above sea level and in terms of geomorphology, the main landscape of the area is a mountain unit. In terms of climate, the city has cold and snowy winters and almost mild summers.

In this study, descriptive-analytical method and software were used to identify flood-prone areas and to evaluate the development of residential areas. The data used include the DEM 30 m, various layers of information provided by the organizations and satellite imagery of the study area. The tools used include ARCGIS, ENVI and IDRISI software. The method used in this study had two stages. In the first stage, WLC and AHP models were used to identify flood-prone areas, as well as the four criteria of river distance, lithology, land use type, elevation, slope and slope direction which has been selected based on the opinion of experts and according to the characteristics of the region. Secondly, in order to evaluate land use change trends and the development of residential areas towards flood zones in the study basin, land use maps were prepared from 1990 to 2019. In the third stage, LCM (Land Change Modeler) model was used to review and analyze the changes and to evaluate the process of land use change and development of residential areas towards flood-prone areas.

In this study, WLC and AHP models were used to identify flood prone areas. The results indicate that many sections of the study area are within the range and risk of flooding. In fact, according to the parameters considered, the final map of flood-prone areas has been prepared and the final map was divided into 5 classes. According to the map, the class of very high is mainly comprised of adjacent river areas, low slope and low altitude areas. This class, with an area of 76.1 km2, covers about 12.3 percent of the basin. The high-potential class also consists mainly of the middle parts of the basin and the areas adjacent to the rivers, and has low elevation and slope. This class, with an area of 145.8 km2, comprises 23.5 percent of the basin. The mean class covers a large part of the basin, so that with an area of 251.8 km 2comprises 40.7 percent of the basin, which mainly consists of low height, gentle-slope areas that are away from the river. The class of low potential has an area of 108.7 km2, comprising 1.5 percent of the basin. The class mainly consists of foothills and the areas far from rivers. In addition, the high-potential class, with a surface area of 37 km2, covers about 5.97 percent of the catchment area, including areas with high elevation and slope as well as offshore areas.

The results of the present study indicate that the city of Nurabad has a high potential for flooding. Given that residential areas such as Nurabad are located in high flood potential classes, attention to these areas and preventive measures is essential. In this regard, the evaluation of land use changes in the study area indicates that in accordance with the increasing trend of population, residential land use has also undergone significant changes. The results of the evaluation of the process of development of residential areas indicate that the land use area was about 2.94 km2 in 1990 year. This has increased to 3.68 km2 in year 2000, to 5.34 km2 in year 2010 and to 6.36 km2 in year 2019. Evaluation of the calculations shows that among the land uses of the study area that have become residential land use, there is a 0/23 km2 orchard, a 3.9 km2 agricultural land use and also a 1.1 km2 pastures and lands are barren lands. In light of the above, in recent years, the trend of residential areas in the city of Nuabad has been moving towards flood-prone areas.