نشریه جغرافیا و مخاطرات محیطی
پیاپی 30 (تابستان 1398)

The occurrence of natural disasters such as floods and earthquakes leave a number of damaging effects in the majority of geographical regions, particularly in rural areas. Given their close connection to the natural environment and their limited capacity, rural areas are more vulnerable compared to urban regions. The essential assets and properties of rural residents are reduced by annual floods which destroy agricultural products, houses, infrastructure, and machinery. Consequently, today’s conditions makes governments shift from focusing on reducing vulnerability to increasing resilience against disasters in order to decrease the effects of natural hazards.
The purpose of the present study is to assess the villages in Chahardangeh region in terms of social resilience against flood. The aim is to offer a number of practical solutions in line with increasing social resilience and ultimately, reducing the severity of damages caused by floods. Accordingly, this study seeks to provide answers to the following questions: What are the most significant social resilience indices in villages under study? What are their significance coefficients? What are the conditions of investigated villages in terms of social resilience? 

Review of Literature and Theoretical Framework
There has been numerous studies conducted on the area of social resilience. Having conducted a study on the social resilience indices of Farahzad District against earthquakes, Heydarian, Rahimi, Fathollahi and Ghafoori (2017) concluded that indices including awareness, knowledge, and social dependency had the highest and lowest effects on the social resilience of this district, respectively. Ebadallahzadeh Maleki, Khanloo, Ziyari and Shaliamin (2017) assessed social resilience in Ardabil City and ranked districts including Touy, Gazran, Aali Ghapou, and Owjadkhan from the 1st to 4th, respectively. Mobaraki, Lalepur and Afzaligorooh (2017) analyzed various dimensions of resilience in Kerman City in addition to institutional, economic, and physical indices. Saja, Teo, Goonetilleke and Ziyath (2018) proposed the “5S model” which involves social structure, capital, mechanism, equity, and beliefs as a comprehensive and compatible framework to measure social resilience. WASPAS technique, however, was employed in the present study which is a combination of weight accumulation and production model and is based upon objective methods in order to assess social resilience; It is more accurate and sensitive compared to other independent methods. Moreover, while the concept of resilience has been discussed through viewing the physical, economic, institutional, administrative, environmental, and social dimensions in previous research simultaneously, the concept is examined in the present research exclusively through a social outlook and with respect to the flooding crisis.

The present study was conducted using a descriptive approach with applied purposes. Data were collected using library and field studies. The total population of the study included a number of villages in Chahardangeh region, most of which are subject to floods. According to the official 2016 census, the total population of rural areas under examination is approximately 1435; however, the sample population of the study was calculated 303 using Cochran’s formula. They were selected via stratified random sampling. The validity of questionnaires was confirmed through the comments of experts; its reliability was also indicated as 0.83 using Cronbach’s Alpha in the SPSS software. Finally, data were analyzed using the FANP model and WASPAS technique.

In this study, the weight of indices were indicated using FANP model. Subsequently, factor analysis was carried out using 24 candid indices (according to theoretical research) via the SPSS software. Ultimately, factors were named using the 5S model of social resilience proposed by Saja et al. (2018). Given the relative significance coefficient of indices, the highest and lowest effects in social resilience across Chahardangeh region belong to neighborhood bonds and willingness to help against disasters with values of 0.0935 and 0.0061, respectively.
Results obtained from WASPAS calculations showed that the highest extent of social resilience against flood belong to Araa and Chaharroudbar villages with values of 0.9184 and 0.9126, respectively, while the lowest value belonged to Zekryakola village with a value of 0.6597.

The results obtained in this study according to the FANP model demonstrated the unequal coefficients of social resilience indices. On the other hand, results obtained from the WASPAS model in assessing the extent of social resilience also showed that the studied villages are at different levels in terms of social resilience. Between villages including Araa, Aliird, Chalou, and Chaharroudbar with high extents of social resilience and villages including Bard, Bandbon, Tillebon, Zekryakola, Saeid Abad, and Ghalehsar with low extents of social resilience, there are a number of differences in terms of social resilience indices which are listed below:
Overall, villages including Araa, Aliird, Chalou, and Chaharroudbar has a younger mean age compared to villages including Bard, Bandbon, Tillebon, Zekryakola, Saeid Abad, and Ghalehsar; therefore, the former group would have a more positive reaction in times of crisis.
Social interactions in villages including Bard, Bandbon, Tillebon, Zekryakola, Saeid Abad, and Ghalehsar has been reduced majorly due to housing instabilities which indicates the lower resilience of these villages in the social aspect.
In villages including Bard, Bandbon, Tillebon, Zekryakola, Saeid Abad, and Ghalehsar, only 46% of the respondents were willing to cooperate after crisis; according to these respondents, the reason for their unwillingness was lack of necessary knowledge in this context.
As a result of the lack of knowledge and awareness, people in villages Bard, Bandbon, Tillebon, Zekryakola, Saeid Abad, and Ghalehsar, saw crisis management as the sole responsibility of governmental bodies. Only 30.7% of the respondents were willing to form NGOs to cooperate before, after and during the occurrence of floods.

Subsequently, the following recommendations are listed in line with increasing social resilience in order to induce more flexibility among rural residents during possible occurrences of floods:
Increasing the local knowledge and the awareness of rural residents with respect to the dangers of flood through attempts made by rural management and Islamic Councils of rural areas;
Providing suitable platforms for the participation of rural residents through holding flood maneuvers with operational and educational purposes rather than exhibitive;
Altering flood paths so that the properties and belongings of rural residents such as houses, animals, and farming and gardening lands remain unharmed;
Holding seminars, workshops, and educational courses on how to prepare and confront possible floods in order to designate and operationalize social resilience indices.

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The flood is one of the most important natural disasters, which is causing significant damage to affected areas. In flood management process, the factors which effectively responsible in flood formation are identified, and then areas with high potential for flood occurrence are identified. Because of the vast extent of catchment areas and the limited economic and administrative resources, the implementation of flood control projects in all flood-producing areas is not feasible. Therefore, prioritizing sub-watersheds is one of the chief measures for sustainable management of watersheds, with the purpose of controlling the flood. Multi-criteria decision-making (MCDM) methods such as the analytic hierarchy process (AHP) method and the analytic network process (ANP) are the techniques for identifying the areas with high flood-producing potential.
Since soil properties, infiltration rate, and quantitative geomorphological characteristics determine the amount of excess rainfall and runoff production, thus, the simultaneous application of morphometric analysis method and decision making models is very useful in the areas with data scarcity. In morphometric analysis, the physiographic and morphological characteristics of the watershed are analyzed based on the digital elevation model and finally the sub-watersheds are prioritized. Dividing large areas into multiple sub-watersheds and prioritizing these sub-watersheds reduce the time and cost of running watershed operations as well as making watershed projects more efficient.
The purpose of this study was to determine the sub-watersheds with critical conditions in terms of flooding risk in Dehbar watershed, Khorasan-e Razavi Province, Iran to reduce the costs of carrying out the watershed management projects focusing on flood control. It is worth noting that due to the lack of required data, morphometric and hydrologic analysis methods were used. In order to prioritize the sub-watersheds of Dehbar watershed, multi-criteria decision-making methods including AHP, VIKOR, and Permutation were employed. Afterwards, the results of these models were compared and verified by regional flood analysis method.
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The study area was Dehbar watershed located in Torqabeh and Shandiz County, Khorasan-e Razavi Province, Iran. The area of the Dehbar watershed was estimated to be 115.73 km2. In order to better identify and evaluate runoff production capabilities, the watershed is divided into smaller hydrological units that have been separately investigated. This classification was made based on the location of the water resources, the location of the villages, the hydrographic network, the topographic contour lines, the satellite imagery, the field visit, and the integrative view in the GIS system, so that through the use of the ArcHydro extension in ArcMap, the Dehbar watershed was divided into 6 hydrologic and 4 non-hydrologic sub-watersheds.
The Dehbar watershed was divided into 10 sub-watersheds. In the current study, 13 evaluation criteria including area, compactness coefficient, drainage density, circularity factor, form factor, curve number, bifurcation ratio, main channel length, average slope, average height, time of concentration, rainfall and runoff coefficient were selected, and the amount of each for each sub-watershed was calculated. The weight of parameters was derived by the AHP technique. After determining the weights of the evaluation criteria and the preparation of decision matrix, VIKOR and Permutation models were employed for prioritization. After prioritizing, the regional flood analysis method (based on existing stations in the watershed) was used to compute maximum flood discharge in different return periods in order to evaluate and validate the considered models.
To this end, a homogeneous area with hydrometric stations was first identified based on geographical and climatic conditions within the region of the study area. The outliers were then eliminated and the frequency analysis was performed for each station individually and the best-fit statistical distribution was identified and selected. Finally, for regional flood analysis, a regression relation was acquired between peak discharges and contributing area in adjacent catchments; thus, it is possible to estimate peak discharge in the study area.
Finally, the outcomes of three employed multi- criteria decision making methods were combined using the average rating method.
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Pairwise comparisons between considered criteria were performed based on AHP method and the relative weight of each criterion was obtained. Runoff coefficient with the relative weight of 0.221 had the highest importance among the considered criteria. Subsequently, rainfall criterion, time of concentration criterion, and curve number criterion were in the following ranks with the relative weights of 0.148, 0.116 and 0.109, respectively. The criteria of average elevation and form factor also had the lowest relative weights. Meanwhile, the inconsistency rate in AHP method was 0.04, indicating that the decision making process is consistent.
After determining the relative weights of each criterion for each sub-watershed, the VIKOR model was applied. According to this method, sub-watershed No.1 ranked first with Q index of 0.9715, sub-watershed No.3 ranked second with Q index of 0.8739, and sub-watershed No. 2 ranked third with Q index of 0.6030. Therefore, these sub-watersheds should gain high priority in watershed and flood control operations. Meanwhile, sub-watershed No. 7 with Q index of 0.0312, sub-watershed No. 10 with Q index of 0.0950 and sub-watershed No. 9 with Q index of 0.3132 were in tenth, ninth and eighth priority, respectively. Thus, these sub-watersheds had the lowest priority for implementing watershed management activities.
In the next step, the Permutation model was employed. According to the results of this model, sub-watersheds No. 1, No. 3 and No. 2 were ranked first to third, respectively. This is due to the high amount of rainfall, runoff coefficient and curve number in these sub-watersheds. Meanwhile, sub-watersheds 5, 8 and 10 were in the lowest ranks.
In addition, the results of regional flood analysis showed that sub-watersheds 1, 3, 8 and 2 had higher flood peak discharge, respectively. Meanwhile, sub-watersheds 5, 10, 4 and 9 had lower flood peak discharge and were in the last priority in terms of potential for flood generation.
In the final step, in order to provide a proper ranking for sub-watersheds, we used the average rating method to combine the obtained priorities by three different applied techniques. The outcomes showed that sub-watersheds No. 1, No. 3, and No. 2 were in the first rank, and therefore, in terms of the need for watershed management measures were in the top priority.
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The results of this study showed that the derived priority of the sub-watersheds using morphometric and hydrological parameters as evaluation criteria to identify flood-producing areas is a suitable and appropriate method. Therefore, it is recommended that, in order to reduce costs and gain optimal outcomes, watershed management projects focusing on flood control should be implemented in the identified sub-watersheds which are in top priority in terms of generating flood discharge. The outcomes also showed that one or two factors alone could not determine the priority of flood-producing capability of sub-watersheds and a sub-watershed with a larger area does not necessarily have the higher potential for generating flood, but the interaction of different factors ultimately determines the priority of the sub-watershed in terms of flood-producing potential.

Landslide is one of the natural hazards caused by multiple factors such as topographic conditions, tectonic activity, climatic conditions and the vegetation of the area. This phenomenon may have many financial and human losses. Landslide hazard may include the degradation of natural vegetation, the degradation of roads and residential buildings, soil erosion, increasing sediment loads, and, most importantly, casualties.
The country of Iran with its mostly mountainous topography, tectonic activity and high seismicity, diverse climatic and geological conditions possesses major natural conditions for the occurrence of a wide range of landslides. Therefore, planning to prevent these losses or at least reduce them is very important and will prevent the loss of national funds. One of the effective solutions to reduce landslide losses is to provide a zoning map, determine the sensitivity of different areas to the occurrence of landslide and identify the high-risk areas. By using hazard zonation maps, it is also possible to identify safe locations for the development of new habitats and settlements and other land-uses such as roads, power transmission lines and power plants at different scales. So far, a large number of quantitative and statistical methods have been used to assess the probability of landslide occurrence and prepare zoning maps.
For example, a variety of statistical models such as logistic regression, analytic hierarchy process (AHP), analytic network process (ANP), artificial neural network (ANN), and fuzzy logic model have been widely applied to create hazard maps.
In this study, a landslide hazard zonation map is prepared for Ispiran in East Azerbaijan Province, Iran, where several landslides have been undergoing in the past. The entire study area is classified into five classes according to the risk of landslide occurrence.
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In this study, logistic regression (LR) model and geographic information system (GIS) were used to prepare a landslide sensitivity map. The study area is Ispiran which is a rural district in the central district of Tabriz County in East Azerbaijan Province, Iran. This area is located in the Urmia basin with an approximate area of 660 km2, 38° 1'N to 38°, 28'N and 46° 14' to 46°27'E.
Logistic regression model is applied to determine the relationship between a dependent variable and independent variables. The procedure is quite similar to multiple linear regression, with the exception that the dependent variable is binomial. In this study, the absence or presence of landslide is considered as dependent variable of the model and affecting factors are entered to the logistic regression model as independent variables. Data set of landslides of the area as well as the maps of the affecting factors including elevation, slope, aspect, land use, geology, rainfall, distance from the roads, distance from the rivers, distance from the fault and distance from the settlements were prepared using topographic maps of scale 1: 25000 and digital elevation model (DEM). After determining the factors affecting the occurrence of landslide in the studied area, the relevant data layers were prepared in the GIS environment. In order to determine the coefficients of logistic regression model, the maps of the distance from the rivers, elevation, precipitation, distance from the road, geology, slope, aspect, land use, distance from the fault and distance from residential areas were standardized and then entered to logistic regression model as independent variables. In this way, the role and relative importance of each of the factors was determined in landslide occurrence. Data layer of past landslides was considered as dependent variable of regression model. In the following, the results of the logistic regression model were evaluated.
The results of regression model were evaluated using Chi Square, Pseudo R Square and ROC measures. The Pseudo R Square index based on the likelihood ratio principle tests the goodness of fitting into the logistic regression. The Pseudo R Square can range from zero to one, the higher pseudo R-squared indicates which model better predicts the outcome.. In spatial studies, Pseudo-R square more than 0.2, can be considered as a relatively good fit. The efficiency of the susceptibility model can be evaluated by ROC index (relative operating characteristic). This index is computed from the ROC curve. The ROC curve is a diagram in which the pixel ratio that is correctly predicted the occurrence or nonoccurrence of landslides (True Positive) is plotted against the supplement amount (i.e. the pixel ratio which is wrongly predicted). Finally, Pearson chi-square is the main test used to determine the significance of the relationship between different categorical variables.
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Previous studies have shown that several factors play a role in landslide occurrence, and most of them are common in different regions; however, the role and importance of each of them in the occurrence of this phenomenon may vary in different regions. According to the coefficients obtained by the model in this study, geology is the most important factor affecting landslide occurrence in the study area. The second affecting factor is land-use.
The model is validated through three measures, including Chi Square, Pseudo R Square and the area under the curve. The results of validation of model are discussed in the following. The value of the Chi Square index is 18.6633, which shows that all coefficients are not zero. The value of the Pseudo R Square indicator is 0.265, which shows an almost acceptable fit of the model. The area under the curve (ROC) is 0.957, which indicates that the observed landslides have a strong correlation with the probability values derived from the logistic regression model.
Finally, based on the best equation between effective factors, landslide hazard zonation map was prepared for the study area. According to the landslide susceptibility, the whole area was classified into five risk classes (very low, low, moderate, high and very high).
The results of the research show that ten percent of the area is located in the high and very high risk classes.
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Preparing a map of the susceptibility of areas to landslide and determining the probability of its occurrence can play an important role in reducing the hazards caused by this phenomenon. In this study, the integration of logistic regression model with GIS was applied for landslide susceptibility mapping in Ispiran, East Azarbaijan Province, Iran. According to the results, the relative importance of geology and land use is more than other affecting factors in this area.

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Nabkhas are sand dunes that form around vegetation. They have been reported in many parts of the world. Nabkhas are often associated with areas having degraded soil and vegetation. In these landscapes, information on the formation, structure and growth of coppice dunes can provide distinct clues about environmental changes. Therefore, to evaluate and control desertification resulting from vegetation change it is critical to understand the coppice dune dynamics. Many studies have showed the importance of aeolian processes, including the erosion of sediments and their deposition under the shrub canopies. This study focused on evaluation of morphoscopy and granulometry characteristics of nebkha sediments in Shahdad-Kerman.
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The study was conducted in the Southwest Lut playa, Shahdad, Kerman province. A coppice dune was selected. The height of the selected dune was measured from the base of the dune to the center of the mesquite canopy, which typically protrudes about 15m. Sediment sampling was conducted on the top 10cm of the sediment. Sediments were sampled every 0.5m coppice dune. The samples were transported to laboratory for determining morphoscopy and granulometry characteristics such as sorting and skewness and sediments physiochemical properties such as EC, pH, SAR, particle size distribution (sand, silt and clay).
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The samples belonging to lower part of nebkha had higher EC than those of the upper parts. Average pH of the samples was 4.23. Mean grain size for sediments on the upper and lower parts of the dune was 208μm and 115μm, respectively. Mean sorting was 1.29. The lower samples are generally better sorted compared with the upper samples. Skewness of grain-size distribution was 0.16-0.82. Mean kurtosis of all the samples was 1.74. The results of kurtosis showed that the upper samples exhibited the lowest kurtosis. The results of morphoscopy showed distance of deposited sediments around Nebkha from their source area was approximately 20-50 km exhibiting their short distance from source area.

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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.

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In different countries affected by snowfall, sub-zero temperatures and freezing temperatures, special maintenance and control is maintained for roads and transportation networks each year with large budgets. The winter severity index is an index that combines different air effects into a single value to help the organization compare and normalize costs geographically and temporally. Researchers such as Strong and Shvetov (2006) have invented models of winter traffic, for example, Qui (2008) on winter road traffic and maintenance, Boselly and Edward (1993) on road maintenance, and Cerruti and Decker (2011) on human activities. Given that much of northwestern Iran is mountainous, the occurrence of freezing and snowfall is not the same in intensity and duration, and the necessary budget is not evenly distributed. Therefore, the necessity of knowledge based on the climatic facts of the region can be an important step in the proper functioning of the relevant organizations in the process of budget allocation for the maintenance and control of the road network in the North West region.
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Study area
Northwest of Iran has some kind of topographical unity (Taleghani, 2013). In spite of the mountainous area, the flatlands and troughs are located in it. The most famous ones include the plain of Moghan, the plains of Tabriz and Urmia, and other plains around Lake Urmia and Lake Urmia itself. In general, the Northwest region has cold and mountainous climate due to its geographical location and topographic dispersion, especially in the cold season. And most of the rainfall in this season is in the form of snow falling and freezing, which is one of the highlights of this season' climate.
Data and numerical analysis

In this study, the mean daily temperature of 18 synoptic stations in Northwestern Iran (see Figure 1) was used from October 1 to the end of March of the following year in the 1986-2015 statistical period. Accordingly, temperatures below zero were assigned a positive algebraic signal and temperatures above zero were assigned a negative algebraic signal. Then, the cumulative amount of frost day degree was calculated during the above months. In this function, if the cumulative amount of frost day degree is negative for a given period, the cumulative value is zero and the new cumulative sum begins the following day. In this regard, it was done based on Asell's (1980) classification to obtain an adequate understanding of the degree of winter severity. Accordingly, the intensity of winter was determined in the study stations and was classified into five groups. Based on this classification, the cumulative frequency distribution of frozen degree-days more than 95% took place as the most severe and less than 5% as the mildest classes. Also, 15% high distances (80 to 95%) as more severe than normal, and 15% lower distances (5-20%) were classified as milder than normal, and 20% to 80% classified as normal.
Synoptic analysis

To measure the changes in the general atmospheric large-scale circulation and to study the impact of atmospheric circulation on extreme climatic conditions, the Mean Circulation Composites data for the period of 1961-1985 as a representative of the past climate and the 1986-2016 as a period with the occurrence of climate change (WMO, 1989) were obtained based on NCEP / NCAR data. The difference maps were then plotted by subtracting the new statistics period from the old statistical period to show the changes in the general atmospheric circulation in these two periods using Grads software.
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Results of numerical analysis
The survey of the mean frost degree day map of the region shows the difference between the regions. Sarab Station with 392 degrees in east and Maku station with 322 degrees northwest and Takab with 324 degrees in the south have the most significant winter intensity, whereas Pars Abad with 13-degree day shows the lowest winter intensity during the statistical period. Around Lake Urmia, especially in the south, relatively low values were observed. In addition, the survey of the map of minimum frost degree day's values of the region indicates the existence of a focal point in the east of the region at the Sarab station. In other words, even in the years when the region has mild winters, the intensity of winter cold at the Sarab station is reduced; however, it significantly differs from other stations. The investigation of the map of maximum frost degree days in the region also shows the intensification of winter cold in east (Ardebil with 773 degree days and Sarab with 709 degree days) and southeast (Takab with 830 degree days) and northwest (Maku with 623 degree days). The examination of the start and end dates of the cold season shows that in the region's severe winter foci, the cold season begins in the third decade of November and lasts until the third decade of March, while it begins in the first decade of December and ends in the first half of February in parts such as Parsabad.
Results of synoptic analysis
The mean geopotential height anomaly map of 500 hPa, for the months of October to March (cold period) from 1988 and 1989 in the study region (northwest of the country) shows that the negative anomaly of geopotential height is even as low as -10 m, which indicates a decrease in the thickness of the atmosphere during this time period. The 1988 surface air temperature anomaly map shows a negative temperature anomaly in the north and northwest of the country as compared to the long-term average, reaching more than -2 degrees in West Azerbaijan Province. This indicates colder autumn and winter than other years, which fully confirms the results of the winter severity index. The cold period anomaly map of 1998 also shows an increase in temperature this year as compared to the long-term average across the country, reaching as high as 3 degrees Celsius for the northern parts of West Azerbaijan province according to results from Calculate winter severity index.
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The surveys of northwestern Iran based on the use of total daily negative temperatures indicate that the Sarab, Maku, Takab stations have been the strong winter hotspots in the region. The most severe winters have also occurred in Ardebil and Sarab stations in the east, while Takab and Maku in the west of the region and the Takab station also have the highest record during the statistical period with 830 freezing degree-days. However, it was found that in severe winter foci, the cold season begins earlier and ends later. Based on the synoptic analysis, it was found that in 1998 the decrease in the thickness of the atmosphere caused more instability; thus, cold airflow at high latitudes and lower air temperatures, and ultimately increased winter cold intensity. The increase in atmospheric thickness in the winter of 1998 resulted in greater stability as well as the intensification of downstream currents due to delayed subtropical high-pressure retreat (STHP), leading to an increase in air temperature and a warm winter experience this year. Accordingly, it seems that the relevant road transport agencies of the country should take the necessary measures to establish fully equipped winter toll in Sarab, and Ardebil in the east and Takab and Maku in the west of region; in fact, with proper management of resources and costs, they work best while saving money.

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Climate change is a complex atmospheric-oceanic phenomenon that is affected by global and long-term human activities. This phenomenon is influenced by factors such as solar activity, volcanoes, atmospheres, oceans and greenhouse gases (of natural and human origin) that interact with each other. Atmospheric pollutants have also been shown to be highly concentrated causing damage to Earth's biological cycles. As the largest and most populous city in the country, Tehran is plagued by air pollution and other factors including climate change, fossil fuel consumption from transportation, household energy and energy industries, and rapid population growth and urban development, and they have no lasting impact. Reviewing the studies on the occurrence of climate change at global and national levels and the occurrence of these changes with varying intensity and weakness in our country. this study seeks to study the effects of climate change on the trend of Tehran metropolitan pollutants in the coming years and achieving appropriate and accurate methods for analyzing the relationship between air pollution and climate change in Tehran metropolis and predicting the trend of air pollution caused by these changes in the coming years.
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The case study of the metropolitan area is Tehran. SDSM scenario forecasting method is used to predict and evaluate future climate change in Tehran metropolis and its relationship with Tehran air quality. The regression method then predicts and analyzes its future relationships with Tehran air quality using two optimistic scenarios RCP 2.6 and pessimistic RCP 8.5. The SDSM statistical exponential model is used to simulate climate data at a station in the present and future under the influence of climate change phenomena. The data in this model are daily time series for climate variables such as rainfall, minimum and maximum temperatures and other atmospheric parameters.  This model is a kind of transitional function models (regression models) and is able to simulate data from 1 to 100 times per run. To do this, a large-scale exponential microscope (GCM) was used in this study to produce daily temperature and precipitation data from
The output of the HADCM3 model wasused. For exponential microscopy, SDSM statistical exponential model wasalso used to simulate Tehran metropolitan climatic data in the present and future conditions. The data used weredaily time series for the minimum and maximum temperatures of rainfall climate. This model is a kind of transitional function models (regression models) and is able to simulate data from 1 to 100 times per run. In this study, two absolute mean error criteria were used to evaluate the model performance.
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 For forecasting and scenario analysis of indices of Tehran metropolitan air pollutants, first, scenario prediction of minimum temperature, maximum and daily precipitation of selected metropolitan Tehran stations (Mehrabad, Geophysics and Shemiran) was done. Then, by averaging the data (maximum temperature, minimum temperature and precipitation) of the selected stations as mean data of Tehran city and using the mean data of Tehran air pollution indices, we analyze and analyze their correlation and regression relations with mean indices. Air pollution in Tehran was discussed. Behavior of Tehran air pollutants in the coming years was also evaluated through scenario prediction.
Exponential microscopy results of Tehran metropolitan daily minimum temperature showed that according to statistical analysis and exponential scaling results of HADCM3 model data, the mean minimum temperature at all stations increased in the period 2017-2047 and the results showed that based on the two scenarios RCP 2.6 and RCP8.5, the average temperature of Tehran in the period 2047- 2017 will reach 13.4 and 13.8 degrees, respectively. However, the RCP8.5 scenario hadmore pessimistic conditions for each station than the RCP 2.6 scenario. It shows the mean minimum temperature and its standard deviation for the period 2017-2047, and the amount of minimum temperature changes at selected stations over the next thirty years showed an increase in temperature in both RCP 2.6 and RCP8.5 scenarios compared to observational data.
The results of exponential maximal temperature scaling for the selected stations also showed that the air temperature have an increasing trend in the period 2047- 2016, based on the two scenarios studied. Exponential scaling of the maximum temperature for different stations has also made it clear that the air temperature will increase over the period 2047- 2016, based on the two scenarios considered.
Based on the SDSM model outputs from the HADCM3 data scaling model, the average monthly precipitation of the stations under study in the period 202017-2047 under the RCP8.5 scenario (pessimistic conditions) was about 20.9 mm, under the RCP 2.6 scenario (good conditions) is about 22.1 mm. However, the average monthly precipitation in the 1991–2016 observation period was about 25.5 mm, indicating a downward trend over the coming years. Therefore, the daily exponential scaling of the stations under study for the period 2017-2047 also indicates changes in precipitation values and based on the output of the scenarios, all the stations under study will experience decreasing precipitation in the coming years.
  In order to evaluate and analyze the scenario of Tehran air pollutants, according to the results of the average scenario data and the correlation and regression method, the scenario data of dependent variables ie Tehran air pollution indices were generated. To do this, the meteorological parameters were selected according to the statistical bases of pollution indices (since 2006) and daily data and their relationships were analyzed.
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Analyzing the trend of Tehran air pollution and its relationship with climate elements using SDSM model under two optimistic and pessimistic scenarios RCP2.6 and RCP8.5, showed that the air temperature trend (minimum temperature and maximum month) in subsequent years increased trend. (Calm). This indicates a general warming of Tehran's air in both the RCP2.6 and RCP8.5 scenarios in the coming years. In the case of rainfall, given the downward trend of rainfall in the above scenarios, the increase in air dryness and the decrease in atmospheric precipitation in the coming years are likely. Scenario analysis of Tehran air pollutants also showed that only some air pollutants (i.e. CO2, O3 and PM10 indices) were significantly correlated with meteorological variables (minimum and maximum daily temperature) that regression analysis and scenario prediction indicated. The data showed that the CO2 index will increase in both scenarios, especially the RCP 2.6 scenario in the coming years, but in the O3 and PM10 indices, their value in the optimistic and pessimistic scenarios didnot show any appreciable increase or decrease in the following years.
Therefore, regarding the occurrence of climate change symptoms, especially positive temperature changes in Tehran metropolis, and the positive relationship between climate change conditions, especially temperature parameters with indices of air pollution and its scenario forecasts, it should be said:- Tehran air pollutants will have an increasing or a constant change in the years to come
- The issue of Tehran's climate change and air pollution and its environmental, social, economic and political consequences should be considered as an important and vital issue for the lives of Tehran's citizens in the planning of Tehran's metropolis as the Iranian capital.

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Land Surface Temperature (LST) is an important parameter in controlling surface heat and water exchange with the atmosphere (Li, Tang, Wu, Ren, Yan, Wan, Trigo, & Sobrino, 2013). Remote sensing images are now one of the most important data sources for estimating LST (Hengl, Heuvelink, Perˇcec Tadi´c, & Pebesma, 2012). But the presence of pollutants in the air, cloudiness and failure of the sensors result in the huge data loss, which is called image gaps. So far, several methods have been proposed to estimate the missing values. These methods are divided into three categories: spatial, temporal or spatio-temporal. Time-based methods for estimating missing data are mathematical calculations, the most famous of which are Savitzky and Golay filters (1964). In addition to simple solutions, more sophisticated methods such as Brooks, Thomas, Wynne, and Coulston )2012) and harmonic analysis of Zhou, Jia, and Menenti )2015) were also introduced and implemented on various remote sensing products. Simple interpolation methods such as the nearest neighborhood, SP-Line method, and Inverse Distance Weighing (IDW) are spatial methods. In most of these algorithms, the weighted average is used. Compared to the simple interpolation methods, the approaches that utilize auxiliary data are more of a researcher's interest. Chen, Zhu, Vogelmann, Gao, and Jin (2011) proposed a neighborhood Similar Pixel Interpolation method (NSPI). Zhu, Liu, and Chen (2012) presented the improved NSPI method named Geostatistical Neighborhood Similarity Pixel Interpolation using geostatistics. Gerber, de Jong, Schaepman, Schaepman-Strub, and Furrer (2018) proposed a spatio-temporal algorithm based on subsetting method for estimating the missing values of MODIS NDVI data. The estimation results are influenced by factors such as the structure of the algorithm, the used scenarios, the type of variable, and the region of study. Finding the right fitting image on cloudy days or providing a long time series are also among the most important factors influencing the results of these methods Kandasamy, Baret, Verger, Neveux, and Weiss (2013). To solve this problem, time series from other satellites are mostly used. These images may differ with the target images in terms of spectral structure and have negative effect on the algorithms performance. Only a few researches like Jang, Kang, Kim, Lee, Kim, Kim, Hirata (2010) have used numerical prediction models outputs in producing continuous spatial-temporal remote sensing data. They concluded that the outputs of numerical prediction models could be used on cloudy days.
The aim of this study is to evaluate the approach proposed by Gerber, de Jong, Schaepman, Schaepman-Strub, and Furrer (2018) to reconstruct MODIS LST images and also to study the feasibility of using MM5 model outputs as an auxiliary data for estimating missing values of the images.
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The study area is North Khorasan, Khorasan Razavi, and Southern Khorasan provinces, northeast of Iran, located between 55 to 61 degrees east and 30 to 38 degrees north. The total area of the region is 313,000 square kilometers and the overall climate is semi-arid to dry Ahmadian, Sheibani, Araqi, Shirmohammadi, and Mojarad (2001).
Two types of data were used in this study. 1- LST images, which are produced by Level 3 MODIS (MOD11A2) with a spatial resolution of one square kilometer and a time interval of 8 days. 2- MM5 model Output data, which were images with spatial resolution of 0.5 × 0.5 degrees for the period of 2000-2010. The data was prepared based on the latitude and longitude of the study area from NASA and NOAA internet pages. In the present study, the Subset-Predict Algorithm (SPA) proposed by Gerber et al. (2018) was selected as the basic method. They used the spatio-temporal approach to estimate the missing values of remote sensing images. This approach  is suitable for a data set with a four-dimensional array structure. In this approach, the missing values are predicted in two main steps: 1. Subset, 2. Forecasting lost values based on subsets. This approach was implemented for MODIS LST images. MATLAB software was used to do this. The inputs of the algorithm were an original image and a series of auxiliary images. In this research, two types of tests were designed to estimate missing values. The first test was performed using time series information of LST MODIS and the second test using MM5 output data. In this study, the Root Mean Square Error (RMSE), Mean Difference (AD), and Determination Coefficient (R2) were used to evaluate the performance of the SPA approach in two different situations.
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The results of simulations show that the SPA method has a good accuracy. The average value of the obtained error is 1.487 degrees Celsius. Meanwhile, Kilibarda, Hengl, Heuvelink, Gräler, Pebesma, Perčec Tadić, and Bajat (2014) reported a mean error of ± 2.5 degrees Celsius in reconstruction of LST images of 2011. Implementing SPA algorithm with the MM5 outputs is less accurate than test 1. This can be due to the uncertainty of the MM5 model in predicting the surface temperature. The visual test of the images showed that the spatial pattern of the LST trend was preserved in estimating the missing values, and the algorithm did not impose artificial pattern on the images. This algorithm has been able to easily reset missing values in most places by maintaining a spatial pattern on the edges and also inside the gaps. Only in the quartile section of the right and above the gap area, the temperature pattern was different from the original LST image. Moreover, in this case, using the MM5 model output, the spatial pattern reflects the temperature trend better than the remote sensing time series.
The spatial distribution map of the mean error in the gap region showed that in most pixels, the error value is in the range of 0 to 2 degrees Celsius. Also, an error of more than 6 degrees Celsius is seen in a small number of pixels in the center of the gap. This may be due to the structure of the method and the subsetting in the neighborhoods or because of the extreme changes in the topography in the area. In contrast to the approach proposed by Chan and Shen (2001), SPA method was accurately simulating missing values at both edges. Given that the maximum error location is in both the center of the gap center, there is likely to be a structural problem in the SPA algorithm that needs further investigation.
Validation of the method revealed that test 1 RMSE value is less than test 2. This means that the accuracy of the algorithm is greater in test 1. According to the AD index, in both cases, the SPA algorithm underestimated the missing values. Also, the implementation of the algorithm with the test 1 scenarios with the correlation coefficient was 0.8% more than test 2.
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In this study, the spatio-temporal SPA method proposed by Gerber et al. (2018) was used to estimate the missing values of MODIS LST and image reconstruction in the years 2000-2010 in the north east of Iran. The results of the SPA method in both cases showed that the chosen method was able to accurately estimate the missing values. They also showed that the obtained error values were within the acceptable range in the temperature data (Ferguson & Wood, 2010). Implementing the SPA algorithm was also less accurate than test 1 with the help of MM5 output maps. The algorithm did not impose an artificial pattern on the images. This algorithm has been able to retrieve missing values by maintaining a spatial pattern on the edges and also inside the gap. On the contrary, many of the existing documentation methods, such as Chen et al. (2004), can't estimate all of the missing pixels.
Error spatial distribution maps show that the highest simulation error relates to a number of pixels in the central region of the gap. The results of this study showed that in the absence of sufficient information for temperature in a region, data from the MM5 model can be used to fill in the missing data pixels and maintain the spatio-temporal continuity of the remote sensing images.

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The physical expansion of cities is a dynamic and continuous process through which the city boundaries and the physical spaces in vertical and horizontal directions are increased both quantitatively and qualitatively. If urban growth is uncontrollable and unplanned, it may negatively affect spatial benefits and ultimately lead to urban expansion. The present study aims to investigate the expansion of Mashhad City in Iran from 2001 to 2017 and then forecast the changes until 2026. A Markov chain is a mathematical and probabilistic method which acts as a random process via which the future state of a pixel depends only on its predecessor and is predicted on it. The direct result of this model is the transmission probability matrix, but in this model no geographic perceptions are obtained. In addition, modeling a single map and representing the spatial distribution of classes is not generated. To solve this problem, the CA-Markov model was designed by John Von Neumann in the 1950s in order to add a spatial feature to the Markov model.
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In the present research, the ETM images of the 2000 and 2009, and OLI images of 2016 from the Landsat Satellite were employed. The images were taken in 28.06.2000, 6. 6. 2009, and 25. 5.2016, respectively. Initially, Landsat Satellite images were geometric and radiometric corrections to reduce the satellite imagery errors. As such, the study area is separated from the images and it is attempted to classify the satellite data. The method used to classify the information is the monitoring method via which educational samples are used to classify the pixels. This means that by defining the specific pixels of each image for each of the classes, classification is performed in the form of the considered classes. Moreover, the maximum similarity algorithm is used for the classification of monitoring. In this method, the reflective value method and each pixel are unknown. In terms of the variance and covariance of a particular spectral reaction class, it is assumed that the distribution of the data of each class is based on the normal distribution around the pixel average of the given class. Practically, the variance and covariance, and the mean of the various classes of each satellite image are calculated for the classification of phenomena; thus, each pixel belongs to a class whose presence in that class is more likely to occur. In order to find out the changes in land use in the study area (Mashhad City), including gardens and agricultural land uses, built-up areas, grassland, and rangelands, the Markov chain module was employed. In the Markov chains, the land-cover classes are used as the chain states. According to this analysis, we always use two raster maps called case models. In addition, the two maps illustrate the time interval between two the images and the predicted interval in the 1401 horizon in the CA-Markov model. The output of the Markov model also includes the possibility of turning the status and matrix of the converted areas in each class, and finally the images are probably conditional for different land use conversions. In this study, Cohen's kappa coefficient (κ) was used for confirming the classification.
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In this research, changes in land cover in gardens and agricultural land uses, and built-up areas, meadows, and rangelands using satellite imagery in a period from 2000 to 2016, and also maximum similarity algorithm, monitoring method and Markov chain model were employed. In the Markov chain model, the cover classes are used as the chain states. In fact, the transmission area matrix represents the number of pixels which are converted from a class to another and has changed from any land-use to another within the same time since 2000-2016.
Based on the CA-Markov model, four floors of the land cover in the land uses mentioned in the strategic perspective document for 1404 (2025) were forecasted. In this regard, it was determined that during the years of 2016, 2009, and 2000, the areas of land ​​uses of the built-up areas were significantly increased, and also the rangelands were expanded relatively; however, the areas of ​​ gardens and agricultural land uses have severely decreased. In addition, the areas of ​​meadows have decreased. The area of ​​land uses in the strategic perspective document for 1404 (2025) is somehow similar to those in 2016; therefore, the gardens and agricultural land uses will change as 90.74%, built-up land uses as 121.57%, meadows as 26.21%, and rangelands as 100%. 
In fact, it was determined that the largest area of ​​transmission in different land uses was in the period between 1999 to 2009 for grasslands, gardens and agricultural land uses, built-up areas, and meadows. In addition, the highest probability of transmission area is in the period from 2000 to 2009 is related to meadows, rangelands, gardens and agricultural land uses, and built-up areas, respectively. The largest transmission area of the mentioned land uses is from 2009 to 2016, which is related to meadows, rangelands, built-up areas, and gardens and agricultural land uses, respectively. The highest transmission area of land uses is within the time interval of 2009-2016 related to meadows, rangelands, built-up areas, gardens and agricultural lands, respectively. It is also revealed that the area of ​​gardens and agricultural land uses was expanded in 2009 as compared to that of 2000, whereas it was significantly reduced in 2015.
The area of the built-up areas during the mentioned years was expanded, and in 2016 (as 359973900 m2) it was significantly increased. Meadows’ area was decreased in 2009 as compared to 2000, but it increased in 2016 as compared to 2009. In 2009, the area of rangelands was decreased as compared to 2000; however, it was again increased significantly in 2016 as compared to the years of 2009 and 2000.
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According to the results obtained from the maps, in the three years of 2000, 2009, 2016, the most changes were related to built-up areas. Therefore, during this period the construction and physical growth of the city were mostly in the northwest direction. Moreover, because constructions usually are done on gardens and agricultural land uses, the decrease in the area of gardens and agricultural land uses, and consequently the increase of the built-up areas can be observed in this part of the city. According to the map of 2016, the gardens and agricultural land uses still remains in the south-east part of the city. One reasons for this issue may be the lack of growth of the city in this direction. One of the important issues in the field of urban planning is how urban spatial development and its resulting pattern are. The pattern derived from the spatial distribution of urban human activities called urban form is changing due to the dynamic and changing nature of cities. Urban growth is horizontal and vertical, and Mashhad has a horizontal urban growth. This form of urban growth over time has led to the disappearance of gardens and agricultural land uses and their transformation into built-up areas, resulting in economic, social and environmental crises. Therefore, land use management planning is one of the most important issues in Mashhad. If land use management planning is done correctly, optimally and accurately, many urban issues and problems may be solved.

1

One of the fundamental and increasing problems in most human societies, which occurs mostly through human activities, is the phenomenon of subsidence. The subsidence refers to the surface depression caused by various natural factors such as the dissolution, the watering of the ice, human activities such as mining, unconventional withdrawal from groundwater or oil. Growing population has led to irregular use of water in domestic, industrial and agricultural uses which, in turn, has led to undesirable quantitative and qualitative effects on water resources. In many plains in Iran, excessive exploitation of groundwater has led to a subsidence event. In recent decades, the increase in the world population, especially in urban areas as an important phenomenon, has created complexities and problems in various fields. Urban areas are particularly vulnerable due to population congestion, buildings and communication arteries. Land subsidence in urban areas is one of the most important issues in urban planning and design. Space-based geodetic techniques that can measure changes in the land-surface position have been significantly advanced over the past two decades with the development of satellite-borne differential interferometric synthetic aperture radar (InSAR) techniques. The InSAR techniques can measure sub-centimeter ground displacements at high spatial detail over regions spanning. The purpose of this research is to monitor the phenomenon of subsidence in Kerman over the course of 15 years, and identify high-risk areas across the city.
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InSAR is a remote sensing technique that uses radar imagery to provide spatially dense measurements of surface displacements the satellite line of sight (LOS) with millimeter to centimeter accuracy. Multiple SAR images are used to generate sets of interferograms to form a time series after a joint inversion. InSAR time series analysis helps reduce the impact of several noise sources (decorrelation, orbital and DEM errors, atmospheric delays, phase unwrapping errors) on displacement rates estimates during the time period spanned by the full dataset with an accuracy for surface displacement velocity at the mm/yr scale. We used 6 ESA ASAR C-band radar images acquired by the Envisat satellite between March 2003 and August 2012 from Track 163 (along descending orbits) and we prepared 2 SENTINEL1 radar images acquired by Soyuz satellite. A small-baseline approach  was used to process interferograms and invert for average displacement rates and evolution through time with the New Small Baseline Algorithm Subset chain, as described in detail by 4 individual interferograms were generated using a modified version of the SARScape and the STRM 30-global DEM.
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The interferometric image was provided by using the radar interference method (Fig. 4). As shown in Fig. 4, number of fringes were created. Since the used satellite (Envisat) works in the C band, and each fringe obtained is 2.2 λ which equals8.2 centimeters, the amount of displacement in the direction of the satellite's view is given by counting the number of fringes. Depending on how the color cycles are observed, the displacement rate also varies, so that if the cycle is yellow-blue-red, moving away from the radar and if the cycle is yellow-red-blue, the shift to the radar has occurred. What is observed in different periods is the increasing trend of subsidence and the decreasing trend of uplift over these years. So, as shown in Fig. 5 subsidence rate has been changed from 7.3 to 7.6 cm per year and uplift rate has been changed 6 to 6.6 cm per year in the study area, between 2004 and 2010. So, as shown in Fig. 6. Subsidence rate has been changed from 9 to 10.6 cm per year and uplift rate has been changed 5 to 4.8 cm per year in the study area, between 2010 and 2017. The spatial pattern of displacements has also changed over the course of the study, so that, the subsidence ranges have increased and that have been transferred to urban areas from around of city. In order to identify high risk areas in Kerman city, a high risk areas map was produced. Accordingly, the city of Kerman divided into four zones including very high risk, high risk, and relatively risky and relatively low risk areas. The results showed that about 700 hectares of the city's territory are in very high-risk and high-risk areas.
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The results of the radar interferometry method show that the different areas of Kerman have had different behaviors over the past 15 years in terms of subsidence and uplift. The subsidence areas were concentrated around the city of Kerman in the period of 2004 to 2007, and the subsidence rate was relatively low. Over the years, more areas of city have been abandoned, and so far. Considering the existence of destructive evidence on the city buildings with high ranges of uplift, it can be concluded that earth's uplift can be as risky as subsidence.

1

Considering the area of crisis management in the textures and inner spaces of cities, especially worn-out textures and unfortunately low-strength materials and incompatibility with the urban transport networkis of great importance in times of crisis such as earthquakes. The main part of worn-out textures is often the primary and historical core of cities; on the one hand for a variety of reasons, includes properties that are regarded as the identity, wealth and heritage of the city and its inhabitants, and on the other, investment in these areas  would be economically viable. However, due to the lack of proper attention of the authorities and the lack of proper planning, these textures are plagued with problems such as reduced livelihoods among residents, social deviations, lack of services and facilities, poor user performance and so on. These problems are associated with physical exhaustion, including low pedestrian width and high maze, large number of buildings lacking provincial systems. The micro-erosion of buildings has increased the vulnerability of the tissues and has doubled the problems and unexpected issues during the unexpected events.
In recent years, disaster reduction agencies have focused much of their efforts on achieving a resilient disaster community in which earthquakes have priority over other disasters due to widespread damage and social anomalies. As societies have resilience to natural disasters, the present study aims at identifying and analyzing the factors affecting urban resilience in times of crisis, identifying the strengths and weaknesses, assessing the opportunities and threats during the hazards, and formulating an appropriate strategy to give resilience to the worn texture of the central core of Ahvaz.
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This study is descriptive-analytical surveys and field studies are used to collect the required data. The statistical population of the study includes experts in the urban planning and architecture of Ahvaz Municipality. Therefore, to come up with a more comprehensive and fact-based view, questionnaires were distributed among the statistical population, and focusing on the experts’ views and opinions the data were collected to indicate the weighting of the indicators. The data were then analyzed using the SWOT model and the weaknesses, strengths, opportunities and threats within the desired range in times of crisis were identified.
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The core resilience of Ahvaz City during the crisis is probably due to the tissue burnout and the presence of valuable historical, cultural and religious artifacts such as the designated Caravanserai, the Mahdian Balcony, the Swan Hotel, the Talibzadeh Mosque and the crossing of the Ahvaz Fault. Lack of attention to the crisis management in this area causes damages to the lower resistance blocks and housing, the damage to historical, cultural and religious monuments and buildings has caused a lot of physical damage to the inhabitants of the tissue in crises such as floods, earthquakes, storms, war. On the other hand, the central core of Ahvaz City, despite its high vulnerability during the crisis, has some potentials as well, such as the existence of railways, subways, taxis and bus stations, hospitals in the vicinity, enhancing relief in the area. The shortage of schools and educational places in the area makes the death toll lower during the crisis. Villas and open spaces can be used in various stages of crisis and relief. The diversity of ethnicities, religions, economic, historical values and the tendency for improvement and modernization in this area increases the officials' motivation to pay more attention to this area in times of crisis.According to the results of this study, the situation of crisis management and the resilience in the core of Ahvaz is in an aggressive-competitive position and in order to become successful we need to use the potentials of the core of Ahvaz and the opportunities effectively.
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The results show that the situation of crisis management and resilience in the central core of Ahvaz is in an aggressive-competitive position. In order to increase resilience in the worn out core tissue of Ahvaz, the following is suggested:
Investigate the zoning of urban housing vulnerabilities, for example, in term of crisis management
Develop comprehensive and coordinated scientific studies to better understand and prioritize the subject and the types of risks facing in the study area
Increase coordination between the responsible organizations and develop programs aimed at enhancing the context and institutional relationships
Proper distribution of large scale business and production centers in relation to adjacent areas to reduce congestion in the study area
Establish a physical, spatial and communication link between the study area and the surrounding area by continuing to integrate the main neighborhoods to provide more effective relief in times of crisis
Finally, develop plans and programs aimed at raising the public awareness of the crisis and the various stages of crisis management

1

Snow is a vital component of the Earth's climate system because of its interaction with the energy flux and surface moisture on a local to global scale. This parameter significantly increases the relationships with radiation at higher latitudes. Analyzing changes in the amount of snow is essential for the assessment of the impacts of climate variability of a region. Snow cover has major effects on surface albedo and energy balance, and represents a major storage of water. The snow pack strongly influences the overlying air, the underlying ground, and the atmosphere downstream. Snow cover duration influences the growing season of the vegetation at high altitudes. A shortening snow season enhances soil warming due to increased solar absorption. While the importance of information on mountain snowpack is clear, obtaining these measures remains challenging. This is in part because snow depth and snow water equivalent (SWE) are both spatially and temporally variable, and mountain regions are generally difficult to access. Snow depth is one of the key variables for understanding the relationship between hydrological cycles. The flow of many rivers, especially during the warm period of the year, is mainly due to snow accumulation, which varies depending on the amount of snow melting in the time series. As mentioned, snow is an important hydrologic variable and acts as a water source in many parts of the world, especially Iran. In Iran, mountainous regions act as water suppliers for arid and semi-arid areas around them, and the coincidence of these conditions is one of the most important reasons for the creation of aqueducts in the country. This study, using the ECMWF data base of the ERA Interim, evaluates the trend and slope trend of snow depth (SD) in northern Iran. The achievements of this research can be useful for studies on climate change, water resources, flood, and agriculture. As a step toward addressing this challenge, we evaluated Methods to increase the efficiency of snow surveys and to enhance remotely derived estimates.
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In this study eleven districts of North Khorasan, Golestan, Mazandaran, Gilan, Tehran, Alborz, Qazvin, Zanjan, Ardebil, East Azarbaijan and West Azarbaijan have been studied. Interim was produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The database is available as an hourly basis since 1979. In this study, the spatial resolution of 0.125 × 0.125 degrees arc for the period 1980-2016 was used. Non-parametric Mann-Kendall and Sen's Slope methods were used to evaluate the trend and trend slope of snow depth.
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The assessment of the depth of snow in northern Iran in January shows that only 0.063% of the northern zone of the country has a significant increase in the level. These areas are more in the northwest of Iran on the border with Turkey, areas with no significant trend. An increase of 3.82% of the total study region has come from this month. These areas are located in the North Khorasan Provinceand near Bojnurd. Areas with increasing trend at 0.05, 0.01 and 0.001 levels have not been observed in northern zone of the country. The northwest regions of Iran on the border between Iran and Turkey, which show an increasing depth of snow, can be attributed to climate change affecting the systems leading to northwest Iran, with snow depth rising. January showed the lowest amount of snow depth for me-Kendall in winter. In this month, the maximum declined trend was 5.58 and the average trend was -14.3. Also, the average slope of the calculated trend in January was 0.03. This indicates that the depth of the snow with a negative slope of 0.07 cm is decreasing.
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The results show that snow depth in the north of Iran in winter is more than 96% of the studied area with decreasing trend. The significant decrease trend at the level of 0.001 in the winter is the maximum trend, and from January to March, the size of areas under the territory of this level increase the meaning of the trend, so that in January, February and March, respectively, 47.99, 56.08, 71.82 percent of the area of the northern zone of Iran has fallen into a declining trend at a probability level of 99.99 percent. Winter season of the Iran regions in the northwest and east, the increasing snow depth was observed that this trend is not incremental but significant. The maximum decreasing trend is snow depth in the provinces of Tehran, Qazvin, Zanjan and East Azarbaijan. In end of April areas with no significant decreasing trend with more than 51% of the same areas. The pattern of snow depth in the spring follows the same pattern in the winter. The average slope of the trend has also declined in line with the trend slowdown in April. On the contrary, the decreasing trend in autumn is based on the statistical results obtained in the study period. Snowfall increases in autumn in October and November, unlike other months in the northern regions of Tehran and southern Mazandaran province, especially in the central Alborz region.