نشریه تحلیل فضایی مخاطرات محیطی
سال هشتم شماره 1 (بهار 1400)

Natural hazards can be considered as one of the most important threats to humankind and nature that can occur anywhere in the world. Natural hazards are one of the main obstacles to sustainable development in different countries and one of the important indicators of the development of world countries is their readiness to deal with natural hazards. Therefore, it is important to pay attention to it and appropriate measures should be taken to reduce the vulnerability of human settlements. Nowadays with increasing population growth, population dynamics and the large number of people exposed to various types of disasters, the need to identify environmental potential hazards and identification of hazardous areas are felt more and more. Meantime, some people may not be aware of potential hazards of their place of residence. So by identifying and evaluating potential hazards and their Risks before the occurrence, we can significantly reduce the severity of the damages and contribute to sustainable regional development. The negative effects of natural disasters can be minimized by the availability of comprehensive and useful information from different areas and Multihazard mapping is one of the most effective tools in this regard.
According to the above mentioned, in this study, the spatial analysis of potential hazards in Silvana district in Urmia County has been studied. This study area due to specific geographic conditions such as position, complexity of topographic and ecological structures, in general, the existence of environmental factors for hazards has been selected as the study area. There have been a number of hazards in the past and assessing of this area is necessary, because of the lack of previous studies. For this purpose, by reviewing various reports and doing field observations, three hazards including Flood, Landslide, and Earthquake are identified as potential hazards of the study area.
For assessing hazards, 12 factors in 6 clusters such as Slope, Aspect (Topographic factors), Lithology, Soil type, Distance to Faults (Geological factors) Precipitation (Climatological factors), River Network Density, Groundwater Resources (Hydrological factors), Land use, Distance to Roads (Human factors), Observed Landslide Density and Seismicity (Historical factors) as the research factors has been selected. For weighting factors, Analytic Network Process (ANP) Method in Super Decisions 2.6.0 software environment has been used. The results of the analysis show that Slope (0.201), Precipitation (0.161), Lithology (0.112), Distance to Faults (0.106), Land use (0.096), Rivers (0.078), Seismicity (0.06), Soil Type (0.055), Landslide Density (0.047), Aspect (0.033), Groundwater (0.03) and Distance to Roads (0.016), Respectively have maximum to minimum relative weight. Then, weighted maps are standardized with using FUZZY functions. For this purpose, Fuzzy membership functions such as Linear, Large and Small has been selected based on each factor. For some factors such as Slope, Aspect, Lithology, Soil type, Rivers density, Land use, Seismicity and Landslide density, Fuzzy linear function has been used. For some others such as Groundwater and Precipitation, Fuzzy large function has been used and for distance to Faults and distance to Roads, Fuzzy small function has been used. Finally, weighted maps were overlay in ArcGIS 10.4.1 environment with Fuzzy Gamma 0.9 operator and potential hazards zoning maps is obtained.
Final results indicate that major parts in the Northwest, West and South of the study area located in high risk zones and 59 percent of the total area exposed to high risk. Based on hazard zoning maps, 44 percent of the area exposed to Flooding, 48 percent exposed to Landslide and 44 percent exposed to Earthquake. Also, 61 percent of the population or 37394 people exposed to one hazard, 7 percent or 3817 people exposed to two hazard and 8 percent or 4914 people exposed to three hazard. According to surveys, only 21 percent of the study area is considered as a low risk area but that does not mean that environmental hazards will never happen in these areas. In general, and based on results, it is concluded that Silvana district has a high potential for environmental hazards. Final results of the research show that potential hazards identifying and preparation of hazard zoning maps can be very useful in reducing damages and achieving sustainable regional development. Therefore, considering the ability of hazard zoning maps to identify areas exposed to risk and assess the type of potential hazards, These analyzes should be considered as one of the most appropriate and useful tools in different stages of crisis management that can be the solution to many problems in preventing and responding to natural disasters and therefore, it is recommended that they be used in the crisis management process.

Snow cover in a basin affect its water balance and energy balance. So, snow cover variation is a major factor in climate change of a region. Study of temporal variation of snowmelt and snow water equivalent depth is very important in flood forecasting, reservoir management and agricultural activities of an area. In the most of the mountainous basins of the country, information on snow cover were not available. Also, the number of meteorological stations in high altitude areas do not match with information needed for snowmelt simulation. Therefore, indirect methods such as the analysis of satellite images to obtain the needed parameters for simulation is necessary, which is the one of the most effective methods in estimation of runoff originated from snow. Using the NOAA satellite data for zoning the snow cover of area started firstly in the USA since the 1961 and continuous until today (spatial and temporal resolution of satellite images increased by starting the MODIS work).
Gamasiab River is one of the important branches of Karkheh basin. Its basin area is about 11040 km2 between latitude 47 degrees 7 minutes to 49 degrees 10 minutes east and latitude 33 degrees 48 minutes 4 degrees 85 minutes north. The altitude of this basin is 1275 to 3680 meters above sea level. In this study, for simulation of runoff originated from melting snow, firstly snow cover in the basin of Gamasiab in 2014 to 2017 calculated by using the satellite images of MODIS in the google earth engine system. Also, air temperature and precipitation data of synoptic stations in the area of study and daily stream flow discharges of Polechehr hydrometric station, from November of 2014 to July of 2017 was used. Then, weather and snow cover area included as the input of SRM for simulation of snowmelt runoff. To obtain the information needed to the model, physiographic characteristics of the basin including the area and different classes of height obtained from the Arc-Hydro and Hec_GeoHMS in DEM maps of GIS software. Then the snow cover areas obtained from the images of MODIS in daily interval that obtained by google earth engine system.
Using the digital elevation map (DEM) and the accession of the Arc-Hydro and Hec_GeoHMS software of GIS, firstly flow direction map plotted. Secondly flow accumulation and stream flow network maps plotted, and by introducing the basin output to the program (Polechehr hydrometric station) borders of the basin identified and classification of the basin accomplished according to the three distinct height classes. Monitoring the snow surface cover during the daily time interval showed that the area covered with snow in winter season. This area decreases as the air temperature increases. The SRM model simulated the snowmelt of Gamasiab basin with good accurately, in which, the percent of volume error or Vd was lose than 2% and the R2   was above 0.9.
The results of this research showed that the using the images of MODIS yields a reasonable estimation of the snow cover area of Gamasiab with local of data. Also simulation results showed the high capability of the SRM in snowmelt runoff of the area under study. Result showed that the coefficient of determination and volume percent of error of model was 0.93 and %0.3 for 2014-2015 and it was 0.9 and 3.33 for 2015-2016 years, respectively. The results of this study, was in consistent with the previous studies fading in which in addition of modelchr('39')s parameters, physiographic characteristics, basin play a major role in the accuracy of the simulation. According to the calculated and observed runoff diagram, in both years of study, peak temperatures begin in March, as the weather warms and the snow melts, and will continue until April. Considering the snow cover, it can be concluded that the main runoff of March Peak is related to snowmelt, but with the change in the shape of precipitation from snow to rain and the warming of the weather, April peak is related to rain. Regardless of acceptable simulation results of the model, the lack of snow survey station in the study area, (yield the model to face with difficulty) in process. To overcome this shortcoming, we used the presumptions of the model and recommended values of the model.

KIntroduction

Karst is the result of the dissolution (physical and chemical) in carbonate (limestone and dolomite) and evaporate rocks. Karst developing is affected by climatological and geological factors. In the other words Karst landscapes and karst aquifers are formed by the dissolution of carbonate rocks by water rich in carbon dioxide waters. Karst aquifers include valuable freshwater resources, but are sometimes difficult to exploit and are almost always vulnerable to contamination, due to their specific hydrogeologic properties, therefore, karst aquifers require increased protection and application of specific hydrogeologic methods for their investigation. The groundwater protection in karst aquifer has a special importance, because the transit time for unsaturated and saturated zone is so quickly that the attenuation of the pollutant. Karst groundwater vulnerability mapping should form the basis for protection zoning and land use planning. A conceptual framework was devised for vulnerability mapping based on this European approach.
Social and economic life of cities such as Nourabad, Alashtar, and numerous rural societies is connected to the Gareen anticline springs. In this paper we used PaPRIKa method for vulnerability assessment in the Aleshtar and Nourabad basins.
 

 The Gareen anticline in the Zagros Mountain range is located in the active deforming Zagros fold-thrust belt and Sanandaj-Sirjan zon. Alashtar and Nourabad karst aquifers are located in the north of Lorestan province. There are several thrust faults with northwest–southeast strike such as Gareen-Gamasiab and Gareen-Kahman Faults. Nourabad unit is composed mainly by gray limestone rocks, embedded marl limestone, recrystallized limestone and pyroclastic rocks. One of the most important features of the structural geology of the Alashtar unit, is abundance of the sedimentary rocks and scarcity of igneous rocks in this area. In other words In the Study basins the main geological formations incloud: Bakhtiarian conglomerate, carbonates of Sormeh, Taleh Zang, Pabdeh and Kashkan Formations.
The groundwater vulnerability assessment methods (PaPRIKa) applied at the test sites were designed specifically for karst aquifers. They are based on various types of information concerning the physical characteristics of the unsaturated and saturated zones, the aquifer structure and its hydrological behavior.
The PaPRIKa method takes into consideration criteria for both structure and functioning of the aquifer. Based on EPIK and RISK resource methods, PaPRIKa method was developed as a resource and source vulnerability mapping method, allowing assessing vulnerability with four criteria: Protection, Rock type, Infiltration and Karstification. The P map (Protection) considers the protection provided to the aquifers by layers above the aquifers: the S (soil texture, structure and thickness), Ca (permeability formations), the Uz (thickness, lithology and fracture degree of unsaturated zone) and E (Epikarst aquifer). Moreover, including the catchments of water losses where the vulnerability is higher. R map (Rock type) considers the lithology and the degree of fracturing of the sutured zone. I map (Infiltration) distinguishes concentrated from diffuse infiltration. Ka map (Karstification development) assesses the drainage capacity and the organization of the karst conduits network.
To  calculate  the vulnerability  index,  the  four  mentioned  maps(P. R. I. Ka)  have been  combined  using  the  following  equation coefficients (eq.1): PaPRIKa Index= 0.2 P + 0.2 R + 0.4 I + 0.2 Ka  (1) eq
Due to the fact that karst geomorphology has a great impact on the quantitative and qualitative characteristics of water resources and the vulnerability assessment of these resources, fuzzy logic has been used to zonation of the Karst development in the Aleshtar and Nourabad basins.  In the fuzzy method used a gamma operator (eq.2):µ Combination= ((Fuzzy Algebraic Sum) (Fuzzy Algebraic Product)) 1-γ  (2) eq The vulnerability map for aquifers was prepared using the software Arc GIS10.4.
 
Discussion and Resultsl:

In the Gareen Antarctic region, due to the availability of suitable Karstification, includes: Lithology, Active Tectonics, Mediterranean climate (with average rainfall of between 454-448 mm and average temperature of 13 C˚) features are formed by various forms of karst such as closed pits (Doline, Swallow Hole, Aven, Polyeh (Peljee), several types of Karrens, dissolution Cavities, small and large Caves and Springs. The most important karst features in this area including Dolines (Solutional, Collapse, Subsidence and Dropout) which are known the Karst Nival. Based on the Karst development zoning map by using the fuzzy logic, 15% of the study area has been developed. Due to the vulnerability based on PaPRIKa method, the Aleshtar and Nourabad basins divided into 5 categories. Resuls show that the vulnerability of the study area is mainly classified as High or Very High, due to the highly developed Epikarst, which minimizes the protective function of the unsaturated zone. There are many karst landforms such as dolines and Swallow Holes that are highly vulnerable.
 

The final evaluation of the vulnerability ground waters in the Aleshtar and Nourabad basins using the PaPRIKa method shows that the study area is divided into five vulnerable (very high, high, moderate, low and very low). So that areas with a very low, low and moderate vulnerability are 27.3%, 22.3% and 20.6% of the basin area respectively. Also that areas with a high and very high vulnerability are 17% and 12.8% of the study area cover, respectively. Due to the lack of soil and plant cover, heavy snowfall and the formation of Karst-Nival (including Dolines) highlands of the Gareen Anticline have a very high vulnerability potential. Validation of the results of the karstic aquifers vulnerability to Electrical Conductivity (EC) data and monthly discharge of springs shows that the Zaz and Ahangaran springs are in a high vulnerability zone. In the aquifer of this springs, Rapid reductions in EC are detected after each recharge period. Also in contrast Rapid increases in EC with reductions in recharge. This situation shows the High developed of this aquifers, as a result, the potential for vulnerability in these aquifers is high.
But in the springs of Niaz and Abdolhosseini in the Nourabad basin, the EC chart has not changed much compared to recharge. Therefore, the aquifer of these springs is less undeveloped or low developed and also less vulnerable.

Precipitation as an important climatic element has many irregularities and fluctuations. Iran, especially its southern half, has significant precipitation fluctuations. Several atmospheric systems are involved in the formation of precipitation in this region from of Iran. Sudanese system is one of the most important precipitation systems in Iran. This system, in different synoptic conditions, enters Iran from different input sources and passes through Iran in different ways.
The important and influential role of Sudanchr('39')s low pressure on precipitation in Iran, especially in the southern part of the country, has been repeatedly demonstrated in numerous studies. But the formation and its expansion have received little attention. These reasons have led to the consideration of the position of Sudanchr('39')s low-pressure synoptic expansion as an influential factor in the southern half of Iran precipitation. Therefore, the position of the expansion of this important climatic system has been investigated separately in the precipitation of the three regions south west, south middle and south east.
 

Two categories of data were used for this study. These data include daily precipitation data from the Iranian Meteorological Organization and the ERA interim gridded data include Sea Level Pressure (SLP) and the Geopotential Height of the 700 HP atmospheric level of the ECMWF. Second category data with horizontal resolution of 0.5 × 0.5°  degrees during 1997-2017 statistical period were prepared.
To achieve the purpose of the study, the southern half of Iran was first divided into three regions: South-West, South-Mid and South-East. After extracting daily precipitation of the selected stations in all three geographic regions, a total of 142 precipitation systems was identified by applying the required criteria. From this number of precipitation systems, respectively, were obtained in the south west 107, south middle 19 and southeast 16, respectively. Then, the source of precipitation systems was extracted using the atmospheric lower level maps. Subsequently, the central core and zone of the first closed curve around the Sudanese low pressure were extracted separately for each group. The main axis of the Sudanese low-pressure trough are also drawn on all rainy day. Finally, the model or pattern of atmospheric circulation in the precipitation systems of the regions is presented separately.
 

The purpose of this study was to determine the position of the central core and the pattern of expansion of the first closed curve around the Sudanese system and the Sudanese system trough in precipitation in each of the three regions of the southern half of Iran. Since the arrangement of precipitation systems may vary in different months of the year, depending on the general atmosphere of the atmosphere, the position of the core, the pattern of expansion of the low-pressure trough and the trough of 700-hPa atmospheric level is analyzed separately each month.
In the synoptic pattern of systems, entering from the south west of Iran, the Arabian Subtropical High Pressure with the southwest-northeast direction is located in the eastern half of the Arabian Peninsula and west of the Oman Sea. In this pattern, the troughs are generally north-south. As a result, the rainfall intensity and intensity of precipitation systems, entering the south west of Iran are higher than the other two routes. The focal point of troughs this route is between 30 to 40° east (Eastern Mediterranean). In systems with South-Mid route, the Arabian Subtropical High Pressure has slightly shifted southward and found a northeast-southwest axis. In this pattern, the Mediterranean troughs are generally northeast-southwest. This pattern causes precipitation in the eastern half of the Iran. Or at least no precipitation in the northwest and west of the Iran.
The synoptic pattern of precipitation systems that enter Iran from the southeast is somewhat more complex. In this pattern, the Arabian Subtropical High Pressure has an unusual eastward shift. So that it is based in India. The troughs of this path showed two completely opposite patterns. In some systems, the troughs in the southwest-northeast direction with the orbital inclination, covers the whole of Saudi Arabia and southern Iran. On the contrary, in some systems the troughs stretch quite opposite to the first group, the northwest-southeast direction.
This asymmetry in the expansion of the troughs should be traced to the general topography of the Tibetan Plateau and the circulation pattern of caused by the presence of the Tibetan anticyclone. Basically Mediterranean troughs are disrupted in their usual eastward displacement after a longitude of 60 degrees. As you can see, the Sudanese low-pressure troughs for the South-East Route lack structural discipline and coordination.
 

The results of this study show that the location and pattern of expansion of the first closed curve around low pressure in different precipitation months and systems of the three zones do not differ significantly in location. Rather, it is the most important system in determining the direction of Sudanese systems, the Arabian Subtropical High Pressure and the pattern of expansion of the eastern Mediterranean trough. In the synoptic pattern of systems, entering from the south west of Iran, the Arabian Subtropical High Pressure with the southwest-northeast direction is located in the eastern half of the Arabian Peninsula and west of the Oman Sea. In this pattern, the troughs are generally north-south. In systems with South-Mid route, the Arabian Subtropical High Pressure has slightly shifted southward and found a northeast-southwest axis. In this pattern, the Mediterranean troughs are generally northeast-southwest. The synoptic pattern of precipitation systems that enter Iran from the southeast is somewhat more complex. In this pattern, the Arabian Subtropical High Pressure has an unusual eastward shift. So that it is based in India. The Sudanese low-pressure troughs for the South-East Route lack structural discipline and coordination. This asymmetry in the expansion of the troughs should be traced to the general topography of the Tibetan Plateau and the circulation pattern of caused by the presence of the Tibetan anticyclone.

Atmospheric particles play an important role in balancing the energy budget of the Earthchr('39')s surface. The Sistan and Baluchestan province because of the specific geographical conditions during the year is witnessing the spread of dust particles caused by dust storms. This paper investigates the spatial changes of this phenomenon in the region to identify the association of dust accumulation and the reasons for these concentrations. In this study, the AOD Index data of the Aqua and Terra Modis Satellite Sensor (MODAL2_M_AER_OD) with 10 × 10 km spatial resolution were used. Then, by using statistical methods, a spatial analysis was done and the temporal and spatial changes trends at 95% and 99% significance level were performed using the nonparametric Mann-Kendall method. The results showed that the maximum concentration of aerosol in areas such as Zabol, Zahak, Hirmand, Hamoun, Iranshahr, Bampour, Jazmurian basin, Chabahar, and Konarak. On average, the highest variations in aerosol concentration were in the southern regions of the province include Dashtiari, Polan, and Chabahar, and the least in the northern part of Polan, Chabahar, Konark, and Bampour areas. The trend of changes was evaluated at two significant levels of 95 and 99%. The results of this section showed that the AOD had a positive and increasing trend in June, July, and August in the areas of Dalgan, Iranshahr, Bampour, Bazman, Mirjaveh, Nokabad, Zahedan, Nosratabad, Zaboli, Qasrqand, Irandegan, and Sib-va-Soran Plain and areas such as Korin, Zabol, Zahak, Sirkan (Bamposht), Hamoun have a negative and decreasing trend. The average changes in aerosol concentration in June, July, and August show a significant increase in the aerosol concentration from 2015 to 2018 up to 0.8.

One of the most important challenges for the human communities is Global Warming. This vital problem affected by Climate Change and corresponding effects. Thus this article attempted to assess the trend of real climate variables from synoptic stations. Daily precipitation, Daily Maximum Temperature and Daily Minimum Temperature have been selected for the Hillsides of Southern Central Alborz Mountains and have been tried to prove climate change and attribute the related forcing such as Greenhouse Gases. The Capital of Iran located in this region and this region has a special occasion, because at least a quarter of Iranian population live in these provinces (Tehran and Alborz) and four big dams located in this region. The Intergovernmental Panel on Climate Change’s defines ‘‘detection’’ of climate change as ‘‘the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense, without providing a reason for that change,’’ while ‘‘attribution’’ is defined as the process of evaluating the relative contribution of multiple causal factors to a change or event with an assignment of statistical confidence. Regional D&A studies provide an insight to local changes in natural systems and may help in planning and developing robust adaptation strategies. Previously, formal detection and attribution have been used to investigate the nature of changes in various climatological variables such as air temperature, surface specific humidity, ocean heat, sea level pressure, continental river runoff, global land precipitation and precipitation extremes. However, almost all of these studies deal with climatological or meteorological variables at the global or continental scale. Studies which have attempted to formally detect and attribute regional hydrometeorological changes to anthropogenic effects are rare. Regional-scale D&A analysis is more difficult because the detection of anthropogenic ‘‘signal’’ in natural internal climate variability ‘‘noise’’ is determined by the signal-to-noise ratio which is proportional to the spatial scale of analysis, especially for real observation data. For overcoming this issue interpolation method (IDW) has been applied to transfer point data to area (gridded) data. The point data gathered from 3 synoptic stations (Mehrabad, Karaj and Abali). Then transferred data have been Standard and Averaged for 3 years. Standard values of annual and seasonal amounts have been computed for individual stations as the average of the standard values of annual and seasonal amounts available 3 years anomaly values. Estimates of annual or seasonal variables anomalies were obtained by averaging the annual or seasonal by 12 or 3 respectively. For detecting and attributing 3 simulation signals (ALL, GHG and NAT) selected from Canadian General Circulation Model (CanESM2.0) of CMIP5 archive subcategories. Space–time series of observations and model simulated variables responses to external forcings (the “signals”) first have been compared qualitatively by computing correlation coefficients between observations and simulations. This simple method does not optimize the signal-to-noise ratio nor provide a quantitative measure of the magnitude of model simulated response relative to that in the observations. Nevertheless, it provides an easy-to-understand view of the similarity between observed and model-simulated changes. Optimal detection and attribution analysis very often requires a reduction of dimensionality. This is typically done by projecting both observations and simulations onto leading empirical orthogonal functions (EOFs) of internal variability and using the residual consistency check to determine the number of EOFs to be retained in the analysis. To produce internal variability for residual test and consistency, Pi-Ctrl Runs have been used. The Preindustrial simulations have high volume, this subject complicates calculation therefore Experimental Orthogonal Functions (EOFs) have been used to reduce the Pi-Ctrl simulations volume and provide situations for Optimal Fingerprint. Optimal Fingerprint method is the best method for Detection and Attribution. Results have been obtained by this manner indicated Global Warming affected the study region by affecting on mean cumulative winter precipitation (0.88), mean spring minimum temperature (0.78) and mean summer maximum temperature (0.76). These numbers are the beta coefficient that named scaling factor. Although the scaling factor for the mean spring minimum temperature affected from GHG signal obtained (0.73), but the GHG forcing alone didn’t have a significant effect on the precipitation and maximum temperature. Also, NAT signal didn’t have significant effect on the region alone, too. The obtained results of this study indicate the earlier studies, such as Wan et al, 2014.

As a stressful stimulus, river sediment is the most significant threat to aquatic ecosystems. To prevent or minimize the damage, three stages of the erosion process should be investigated (Naseri et al., 2019: 83). Determining the amount of sediment transported by rivers is important from different aspects. Sediment carried by water flows is considered a factor effective in shaping the geometric structure and geomorphic characteristics of rivers (Tashekabood et al., 2019: 282).

Data and methodology:

To estimate the amount of annual suspended sediments, the flow and sediment statistics of hydrometric stations (8 stations) and meteorological stations (13 stations) were employed (Figure 2). The research statistical period is 25 years (1993-2017). The altitude, area, and perimeter of the basins were obtained from topographic maps with a scale of 1.25000. To investigate the correlation between independent and dependent variables, the normality tests of Shapiro-Wilk and Kolmogorov-Smirnov were performed in SPSS16 software. To extract the geomorphic features of the basins, the digital elevation model was used. Then, ground surface corrections and pretreatments such as removal of hydrological pits were performed and ground drainage pattern was determined.

Stepwise multivariate regressionl In the present study, stepwise multivariate regression was used to reduce the number of independent variables and determine the effective factors in the sedimentation of the basin. This method investigates the effect of several independent variables on a dependent variable (Zare Chahuki: 2010). In stepwise multivariate regression, the independent variable that has no more significant effect on the dependent variable is removed from the analysis, hence excluded from the equation. The general form of the stepwise regression equation is:Equation 1   Y= a + B1X1 + B2X2 + …… + BnXn + e

Data description and interpretation:

The principal component analysis method was used to determine the most effective characteristics of sediments as well as their grouping. In principal component analysis, variables that have a high correlation and are distributed in a multidimensional space are reduced to a set of non-correlated components, each of which is a linear combination of the main variables. The obtained non-correlated components are called principal components (PCs). Prior to component analysis, the KMO coefficient was used to ensure the appropriateness of the data for principal component analysis. This coefficient fluctuates in the range of zero and one and if its value is less than 0.5, the data will not be suitable for principal component analysis and if the values of this coefficient are between 0.5-0.69, The proportionality of the data is moderate and if the value of this coefficient is more than 0.7, the data will be quite suitable for performing principal component analysis.

Regression analysis results:

In this study, the sediment weight of the basin was considered as a dependent variable and other parameters as independent variables. The variables of slope, precipitation, basin length, Elongation Ratio (R), circularity coefficient, and unevenness of the basin have a higher correlation with the amount of sediment production in the basin than other variables.
An eigenvalue was used to determine the number of factors. The minimum eigenvalue for the selection of final factors is 1, and factors with an eigenvalue bigger than 1 are considered final factors. The results showed that the three factors of circularity coefficient, compactness coefficient, and basin form coefficient have an eigenvalue bigger than 1.

The results showed that geomorphic parameters have a high correlation with the amount of annual sediment. The results showed that seven factors of slope, precipitation, basin length, elongation ratio, circularity coefficient, unevenness coefficient, and form ratio of the basin were the most important in estimating the amount of suspended sediment based on the principal components analysis method. The average of special sediment varies from 134 tons per year in Dehbar basin to 16 tons per year in Kardeh basin and also the average annual sediment varies from 261.6 tons per year in Golmakan basin to 156.7 tons per year in Shandiz basin. Evaluation of Bartlettchr('39')s test of sphericity tests and KMO values is 0.9. Therefore, the data is suitable for factor analysis. The percentage of variance explained by each factor indicates that the circularity coefficient with 50.71% of the variance explains all the research variables. In total, three factors of circularity coefficient, compactness coefficient, and form ratio of the basin could explain 82.6% of the variance of all research variables. Therefore, the results are consistent with Lu et al. (1991), Sarangi et al. (2005), Tamene et al. (2006), Zhang et al. (2015), Salim (2014), and Ares et al. (2016).
Khorram Dareh sub-basin with heavy rainfall (504 mm) has the lowest specific sediment, which is due to the geological structure of the region. Based on the calculated indicators, most of the studied sub-basins are elongated. The form ratio of the basin is less indicative of the elongation of the basin. The highest branching ratio of the basins is in the vicinity of faults. Also, high circularity values indicate points prone to sedimentation. River sections up to degree 3 are located in more subdued areas and have a steeper slope. Golmakan, Khorram Darreh, Zashk, and Dehbar sub-basins have a high potential for sedimentation. Regression equations of sediment measurement curves are usually used in sediment load estimates. The most important reason is the ease of application of these equations. According to the research results, it can be concluded that the integrated use of principal component analysis, cluster analysis, and multivariate stepwise regression has a suitable and acceptable efficiency in estimating suspended sediments. Testing the regression model concerning different climatic and hydrological regimes of Iran’s watersheds to achieve an efficient pattern of using these equations can be fruitful in estimating sediment load in different regions.

Changes in OLR can be considered as a critical indicator of climate change and hazard; studies have shown that since 1985, long-range radiation has increased the output of the Earth and is a cause of increased heat in the troposphere. This has led to an increase in drought and a slight decrease in the cloud in the upper terposphere, as well as an increase in Hadleychr('39')s rotation toward higher latitudes. On the other hand, clouds play an important role in the long-wave changes of the Earthchr('39')s output and are adequately evaluated at the global energy scale at all spatial and temporal scales.

Data and methods :

In the present study, in order to calculate the variability and the pattern of seasonal spatial dependence of the long-range radiation output of Iran, OLR data from 1974 to 1976 were daily updated from the NCEP / NCAR databases of the National Oceanic and Oceanographic Organization of the United States of America. To calculate Iranchr('39')s long-range output radiation, in the Iranian atmosphere (from 25 to 40 degrees north and 42.5 to 65 degrees east), using Grads and GIS software. First, the general characteristics of the earthchr('39')s long wave were investigated. To obtain an overview of the spatial status of the seasonal changes of the long-wave and its variability over the country, the average maps and coefficients of the long-wave variations of the earthchr('39')s output were plotted in the spring, summer, fall, and winter seasons. In this study, the slope of linear regression methods using mini tab software was used for trend analysis. Hotspot analysis uses Getis-Ord Gi statistics for all the data.

Explaining the results:

The results of this study showed that the mean of long wave in Iran is 262.3 W/m2. The highest mean long-range radiation output in spring, autumn, and winter is related to latitudes below 30 degrees north, especially in the south and south-east of Iran, with the highest mean in autumn and winter with wavelengths. High output 282-274 W/m2 as well as spring with mean W/m2 295-291 below latitude 27.5° C, which is in Sistan and Baluchestan provinces, south and southeast of  Fars. Hormozgan has also been observed; the lowest OLR average in these seasons is observed above latitude 30 ° N in the northwestern provinces with the lowest mean in the season Yew and winter with mean long wavelength output 213-225 W/m2 and also observed in spring with mean 226-235 W/m2 at latitude 37.5 ° C and latitude 44 ° N in Maku and Chaldaran Is. In summer, the highest OLR averages of 316-307 W/m2 are observed in east of Iran with centralization of Zabol, Kavir plain and Tabas desert as well as west of Iran in Kermanshah, Khuzestan and Ilam provinces, with central length The latitude is 47.50 degrees north and latitude 32/32 east in Ilam province in the city of Musian, due to desertification, saltwater and sand, as well as the absence of high clouds, indicating an increase in the frequency of earthquakes and It is a drought that will lead to shortage of rainfall and increased rainfall in these areas; the lowest average long-range radiation output in summer with W/m2 235-226 extends as a narrow strip from southeast to Chabahar and extends to the middle Zagros highlands in Chaharmahal Bakhtiari province and northwest areas in Maku, Chaldaran, Khoi, Jolfa, Marand, Varzegan, Kalibar, Parsabad, Ahar and Grammy cities. It has also been observed in the northern coastal provinces of Iran including Mazandaran, Gilan, Astara, Talesh, Namin. According to the trend of long-wave radiation output of Iran increased by 0.16 W/m2 and decreased by 0.37 W / m2 with increasing latitude. Seasonal trends indicate that 100 percent of the country has a significant increase in winter and no significant fall in autumn. 21.24% in summer and 18.35% in spring have no significant decreasing trend, which in south-east includes Sistan and Baluchestan, Kerman, Fars and Hormozgan provinces and 78.76% in summer and 81.65% in summer. Spring has a significant non-significant upward trend. The spatial dependence of the hot spots on Iranchr('39')s long-wave radiation at 90, 95 and 99% confidence levels is 45.49% in spring, 37.57 in autumn, and 44.55% in winter. The high wave radiation of summer is 42.2%, which is observed in north of Sistan and Baluchestan province with central Zabul and in east of Lot and Tabas desert and in west of Ilam province with central of Musian. But in spring, autumn and winter in the south and southeast of the country including Sistan and Baluchestan, Hormozgan, Kerman, South Fars, Bushehr provinces and in central Iran including Lot Plains, Desert and Salt Lake and Tabas sandy desert. It is also observed in western Iran in Ilam province, so that these areas correspond to the tropical belt at latitude 30 degrees north. This is due to its location in the subtropical region, the low latitude of Iran, especially south and southeast to central Iran including Lut Plain, Desert and Tabas Desert due to its proximity to the equator, the angle of sunlight is higher and perpendicular. Spun. The spatial dependence of cold spots on long-wave radiation at 90, 95 and 99% confidence levels in spring is 33.44%, autumn is 41.41% and in winter is 44.55%. Cold spots of long-wave radiation are 25.5% in the summer, located at latitudes above 35 ° N in the subtropical belt and include northeast areas in North Khorasan Province in the cities of Bojnourd, Esfarain, Jajarm, Mane and Semlaghan, Safi Abad and northern coastal areas in Golestan, Mazandaran, Guilan, and northwestern provinces of Iran including Ardabil, East and West Azerbaijan, Qazvin and Zanjan North Tfaat Kvh Hay Zagros includes the provinces of Kurdistan, Hamedan, Markazi, Qom, Kermanshah North East part. Minimum OLR cold spot with average output longwave radiation of 213 W/m2 220 northwest of Khoy, Maku, Chaldaran, Jolfa and Marand can be an indicative role for determining convective activity and dynamic / frontal precipitation.

The high spatial and temporal limitations of TIR images for use in urban climatology have been identified as a current scientific challenge. Therefore, the use of Data Fusion Algorithms in Remote Sensing has been considered. In the old methods, two bands of one sensor were used for Data Fusion. In these methods, a panchromatic band was used to increase spatial accuracy, so only spatial resolution was increased. To solve this problem, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was used to integrate the images of two Landsat and Modis gauges to increase the spatial and temporal resolution of the reflection. but, this algorithm is designed for pixels and unmixing areas that are the same in Modis and Landsat pixels. The use of this model was not suitable for urban areas with a different of landuse. Therefore, the Enhanced STARFM model (ESTARFM) was developed. The ESTARFM model was improved in 2014 to predict thermal radiation and LST, taking into account the annual temperature cycle and the unevenness of the earthchr('39')s surface, and the SADFAT model was introduced.
In this study, the performance of SADFAT model in the use of OLI spatial resolution and MODIS temporal resolution in LST forecast in urban areas was examined. The metropolis of Tehran has different surface covers and multiple microclimates. So if the algorithm works successfully, This model can be used in other cities to improve urban heat island studies. The inputs for the algorithm are thermal radiance of Modis and Landsat   images, the red and near infrared band of Landsat for daily production of LST in 2017 in the city of Tehran. The algorithm uses two pairs of Modis and Landsat images at the same time and sets of Modis images at the time of prediction and then calculate the conversion coefficient for relating the thermal radiance change of a mixed pixel at the coarse resolution to that of a fine resolution. In this way, LST is generated in areas with a variety of landuse.
All the estimated pixels were compared to the base image pixels in that range to evaluate the results of the model. The comparison results for the autumn days with the average correlation coefficient of 0.86 and RMSE equal to 0.122, showed that the model has the highest accuracy in this season and in other seasons with the average correlation coefficient of 0.76 and RMSE about 0.4, has provided good accuracy.
Visual interpretation of the results of SADFAT showed that this model is able to accurately predict the LST of the land cover in different surface coatings and even in areas where one or more urban land uses are mixed in one MODIS pixel.
However, the borders are well separated and the features are not combined. Although the boundaries are clearly defined, in some land uses, the predicted LST is somewhat higher than the observational image.
Landsat and Modis satellites pass through an area with a small time difference, so they are suitable for combining with each other. But in predicting reflectance with the SADFAT algorithm, there are systematic and variable errors that we need to be aware of in order to increase the output accuracy. One of the systematic and unavoidable errors is the instability of the Terra and Aqua satellites passing through at any point, ie at each satellite pass, the location of the study area in Swath and the size of the pixel changes. Due to the distance of the study area from the vertical center of measurement on the ground (Nadir), the amount of this error varies on different days and should be checked for each day. The preventable error is the sudden change in one or more images used (16 days of the same pass time interval for Landsat) is high for estimating surface reflectance with spatial and temporal resolution. These changes may be due to human factors such as air pollution or natural factors. Natural factors such as clouds and dust storms are the main sources of error in using the SADFAT model because they are sudden and temporary and cover a wide area. The occurrence of these two factors has a great impact on reflectance. Therefore, a sudden change in these factors, in one or more images, causes a large error in the calculations.
The study also found minor spatial errors in the prediction, so that even on days when the results were better, points were observed where the values ​​in the predicted LST images did not match exactly with the OLI sensor. The reason for this may be due to changes in vegetation. Although there are some systematic and variable errors in the images and the implementation of the algorithm The results of this study showed that the performance of this model is reliable for predicting the daily LST with a spatial resolution of 30 meters in Tehran.
This method is able to support urban planning activities related to climate change in cities, so it is recommended that its performance be examined separately for different land cover in the city and the efficiency of this algorithm be evaluated with other sensors such as Copernicus Sentinels.

Occurrence of natural disasters such as drought, floods and earthquakes in geographical areas, especially in rural areas, often have devastating effects. Hence, resilience has become doubly important, especially in special areas that are of special importance and sensitivity. On the other hand, border areas have a special place in policy-making and planning is important in this areas. One of these areas is Zahak county in Sistan and Balochestan province, which due to the instability and reduction of the inflow of river water resources, as well as climatic conditions and drought in combination with other factors, the traditional employment opportunities of the often rural population face serious challenges and therefore the county5b is deprived. Increasingly, the sustainability of livelihoods is facing problems. The question is, how do the spatial zones and the villages located in these zones react to the change of internal and external natural factors? Which areas and villages are more resilient?
 
Method of research:

This article deals with the spatial analysis of environmental resilience in Zahak county and its purpose is to investigate the differences in resilience in different areas of the county. The general approach to the study is integrated and descriptive-analytical in terms of method. Data were collected using documentary and field methods with observation tools and questionnaires and findings of a specialized panel. The statistical population of this research is the villages of more than 20 households in Zahak city that have had governor of a rural district or village council.
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Description and interpretation of results:

The villages of Zahak county are threatened by the threat of these resources due to their dependence on natural resources. The results show that none of the defined geographical areas in the rural area is sustainable and three rural areas are semi-sustainable and one rural is unstable. Assessment of sustainability in micro zones also shows that naturally unstable villages are often sparsely populated, which means that activity has not developed either. After qualitative and quantitative evaluation of various natural and environmental indicators in the county and their impact on the resilience of places and settlements in the county, settlements and places in terms of resilience were classified into three levels of high, medium and low resilience. In total, 46.7% of settlements and places are at high level of resilience, 37% at medium level and 16.3% at low level of resilience. After matching the settlements and places with the geographical area of ​​the village, three of the four geographical areas are in transition and one is unstable. This study shows that the resilience of individual villages cannot perform well when it is located in areas surrounded by villages with low resilience and the whole area becomes unstable. Thus, in special areas such as Zahak county, crisis management should focus on providing natural resources and preventing vulnerability to natural crises, and it is expected that with natural stability, housing and activity will be sustainable.

Modernization processes and modern urban planning policies have had significant effects and consequences on the spatial transforms of cities in the world and Iran. Among that processes, we can mention the growing gap between social groups and urban spaces based on a number of contexts and mechanisms that, from the late 1960s onwards, have been conceptualized and measured experimentally under what is called the “spatial mismatch hypothesis”. The basic methodology for estimating the state of spatial mismatch in cities or urban regions is based on the logic of “spatial segregation” between social groups and land uses simultaneously; Because based on the spatial mismatch hypothesis, it is not possible to explain the segregation mechanisms between social groups in the city without considering its relation with segregation mechanisms in urban spaces or land uses, and vice versa. Based on such methodological logic, the present paper has assessed the state of spatial (mis)match in Region 4 of Tehran Municipality. The method of data collection was in the form of libraries and data available in the Statistics Center (General Census of Population and Housing in 2016 and at the level of demographic blocks of the region), Road and Urban Development Organization, Municipality of Region 4. Variables used to analyze the spatial mismatch in the region
The level of education, employment in study abroad and inside the country, employment and unemployment status, level of housing infrastructure, type of housing ownership, changes in land use pattern and the amount of daily commutes in the study area.
 Findings obtained based on the defined variables and techniques used in Segragation Analyzer and ArcGIS software show that the state of spatial mismatch in this urban region (like many other cases in cities around the world) is high, but its intensity is higher in terms of job and literacy of social groups in relation to the state of activity and residential land uses. Relying on such findings, some strategies and policies have been proposed to reduce the state of spatial mismatch in Region 4, and to contribute to a more even and equitable distribution of development in this region and hence reduce poverty among the lower classes.

Explanation of the subject: The annual drought phenomenon, by affecting economic, social and environmental issues, leads to the vulnerability of urban and rural households and the instability of their livelihoods. Yazd is one of the provinces with drought. Consecutive droughts in the province necessitate integrated management and community adaptation in times of drought.

Taking into account the length of the statistical period of 20 years and to obtain the results with a high level of confidence, the main data of the census documents that have been compiled for the development of cities and villages have been used. By analyzing the main components of several factors, it was selected as the main components. By calculating the standard precipitation index in the arid region, the driest year was determined and by calculating the weighted average of their correlation index with the main components of socio-economic and ecological environment based on appropriate statistical inference. At the end of the year, the effect of drought on different dimensions was presented by step-by-step linear regression, analysis and communication between them to adapt and resilience of individuals in society.
 According to the general results, one of the most important economic and dry economic losses is the annual income of the villagers, which can be due to the decrease in the area under cultivation and production of the main agricultural products. In the social sector, people with knowledge and awareness should increase their adaptive capacity to the occurrence of drought, in order to reduce the vulnerability of social issues to the phenomenon of drought. The results show that unemployment insurance has increased following the drought. The main reason for this is the unemployment of farmers affected by drought, so changing jobs along with temporary migration or the production of handicrafts, etc. can increase the relative income of households at the time of occurrence and prevent unemployment in these conditions. Increasing unemployment will cause other social harms such as poverty, declining health, increasing disease, and reducing judicial and social security. According to the results, one of the components that has established a high standard of rainfall during the drought year is the theft of livestock, which shows a decrease in the social security of the community. People in the study community increase their adaptability to the annual drought by increasing breeding work, such as rangeland improvement, rainfall collection, biological improvement, afforestation, and irrigation reform.