Journal of Spatial Analysis

The City-region of Tehran is encountered with various environmental problems, including traffic, air pollution, lack of drinking water and green space, physical texture conflict, flood and earthquake. Capital accumulation has considerable role in shaping spaces which can create and intensify environmental disaster in special socio-economic situation. The second cycle is the conversion of capital to fixed and long-term assets with the aim of further benefits, which in fact produces two types of artificially environment during this process namely the built environment for production, and the built environment for consumption.

The third cycle is aiming to connect science to production and increase production capacity by investment in science and technology. When production of surplus value reduced in the first cycle, surplus value of second cycle increases through speculation and real estate transaction (real estate capital). Therefore, the owners of the lands and buildings are encouraged in production, trade and supply of these type assets.

In the courtiers that are legally and administratively encounter with tax receipt problems,  urban lands ownership is deposited to market system without any control, hosing transaction continues without any limitation, situation is moving forward to personal vested interests, asset value rises rather than production value, the price of land and construction increase severely. In the above mentioned condition, beneficiaries attempt convert the natural resources including park, mountain, river privacy and road privacy to marketable commodity and legally or quasi legally seize them. Therefore, unreasonable construction and population density increases and city-region will encounter with environmental disasters.  

The main objective of this research is to understand the underlying factors of capital accumulation through construction and its impacts on createion and intensification of environmental disasters in the Tehran city-region.

  Five different regions of Tehran were selected for data collection. "Q-methodology" was used for gathering and analyzing data. The society of communication or people whom the study sought to identify their mentality towards the research topic, were 25 experts selected through purposive sampling. To set the concourse of communication, a combination of primary (experts commented in an interview) and secondary (sources of credit) sources have been used and   34 statements have been developed. After sorting the data for analysis, SPSS software data matrix is ​​formed. Factor analysis, as main method of analyzing Q data matrix has been used based on Q logical methodology.

The results of Q analysis depicted four viewpoints with variance of 95.911 percent on the underlying cause of capital accumulation through construction and its contribution on increasing risk of natural hazards in Tehran city-region.

The first viewpoint has devoted 52.800 percent of total variances and can be titled as" Function of real estate transaction and Non-productive economic domination".

The second viewpoint which has received 18.914 percent of total variances is accordance with "commodification of land and housing". The third viewpoint is" management and monitoring of the city-region space" with 15.163 percent of total variances.  The fourth viewpoints under the title of" monitoring and control of natural resources" has assigned 9.034 percent of total variances.

As result of these processes, land and housing business have weakened society's productive capacity by extensive land use change in the urban peripheral area's due to its huge and quick profit. The above process accompany with selling excess density policy created a powerful political and economic stratum which harmed city sustainable development. The mountainous area of north, north east and west of Tehran, have annexed to metropolis as a result of above mentioned regulation and  may gardens have converted to construction by different gropes and institutions.

Q method analysis depicted that the Tehran City-Region has converted from use value to exchange value. It means that values of the city including work, security, education, leisure and welfare have been lost in favor of exchange value. In other words, the city has been converted to a commodity for exchange and selling in pursuit of profit, rising cost of urban land, building and housing. Consequently, the city-region construction site is extended to the river beds, steep slops and surrounding natural environment. This in turn is leading to rapid land use change and violation of environmental and spatial rules and regulations and intensification of environmental hazards.

Resilience are concepts that are finding increasing currency in several fields of research as well as in various policy and practitioner communities engaged in global environmental change science, climate change, sustainability science, disaster risk-reduction and famine interventions (Vogel, et.al, 2007). Where the risk is a probability of damage, injury, liability, loss, or any other negative occurrence that is caused by external or internal vulnerabilities, and that may be avoided through preemptive action (Benson, et.al, 2004). Among natural disasters, earthquakes, due to the unpredictable nature of these events, are one of the most destructive. Iran is one of the most earthquake-prone countries in the world that its cities most affected by this phenomenon. Among the cities of Iran, Tehran, as the country's first metropolis, due to dense population, poor physical development, structural density, and lack of standards, is potentially facing a serious threat. The purpose of this study is to investigate the spatial flexibility of Tehran over the region 12 after the earthquake incidence.

The present study is dealt with the data preparing and analysis using geospatial methods. The several geospatial data such as Peak Ground Acceleration (AGA) map, urban structure, infrastructure and population collected from Tehran Disaster Management Center were provided and analysis based some GIS known algorithms. In other to urban spatial resilience zonation the AHP (analytical Hierarchy Process) was implemented to generation risk map. Finally OWA (Ordered Weighted Average) method was implemented in order to Production spatial flexibility map of earthquake incidence over the District 12 of Tehran. AHP model uses of priorities straight experts, but OWA provides of control the level of compensation and risk-taking in a decision. Using the conceptual of fuzzy quantifier with OWA makes the qualitative data analysis enter to decision.

    According to flexibility of the final map with fuzzy operator (All) equivalent to the operator MIN, the worst result Was obtained and resulting the highest risk and lowest flexibility respectively (Districts Nos. 2,12,7,8 and 11).By taking all the criteria of a criterion without compensation by other criteria as "non-risk" is obtained .

Map obtained with fuzzy operator (Half) has the high potential to provide suitable options,  because in addition to integration criteria the importance of each parameter based on the weight given to the criteria are considered. In this map Districts Nos.2.6 and 8 (Baharestan, Emamzadeyahya and Sanglajedarkhangah) respectively were most Risk to earthquakes and therefore less flexibility to the earthquake. The map obtained with the fuzzy operator "Atleast one" is equivalent to MAX operator districts Nos. 2,12,7 and 8 (Baharestan ,DarvazehGhar of Shush,Abshardardar and Sanglajedarkhangah)  respectively were most Risk to earthquakes and therefore less flexibility to the earthquake.

The fuzzy conceptual map quantifier showed that districts Nos. 2 and 12 (Baharestan and DarvazehGhar of Shush) were most vulnerable and therefore less flexibility to the earthquake as final results.

Due to the growth of industries and factories, deforestation and other environmental degradation as well as greenhouse gases have been increasing on the Earth's surface in recent decades. This increase disturbs the climate of the Earth and is called climate change. An Increase in greenhouse gases in the future could exacerbate the climate change phenomenon and have several negative consequences on different systems, including water resources, agriculture, environment, health and industry. On the other hand to evaluate the destructive effects of climate change on different systems, it is necessary to initially study the area affected by climate change phenomena. One of the most important effects of climate change on water resource is Drought.  On the other hand, one of the most serious consequences of climate change is how it will affect droughts and water resources.

Drought along with warmer temperature and less precipitation will threaten the water supplies for the crop irrigation, which will directly reduce the production of crops.The climate of the 21st century will very likely be quite different from the climate we observed in the past. The changes will continue to be large in the future period with increasing carbon dioxide emissions. Analyzing and quantifying the signal of climate change will be much in demand considering the above sectors, which are highly relating to the sustainability and human living.

In the past several decades, global climate models have been used to estimate future projections of precipitation [Intergovernmental Panel on Climate Change (IPCC), 2007], and have led to future estimation of drought, to quantify the impact of climate change and comparing the duration  and intensity of droughts under future climate conditions with current climate by using Atmospheric-Ocean General Circulation Models AOGCMs to predict future Precipitation. Global circulation models namely, coupled Atmosphere-Ocean Global Climate Models (AOGCMs) are current state of the art in climate change research. in This study aims at investigating the impact of climate change on droughts conditions in Iran using the Standard Precipitation Index (SPI).

The precipitation time series have been used for the estimation of Standardized Precipitation Index

(SPI) for three timescales, 3, 12 and 24 months, for the region. The outputs of HadCM3-A2 and A1B were applied for the assessment of climate change impact on droughts. One of the major problems in using the output of AOGCMs , is their low degree of resolution compared to the study area so to make them appropriate for use, downscaling methods are required. In this study we have used lars WG for downscaling monthly average of rainfall of AOGCM-HadCM3, and The HadCM3 outputs were downscaled statistically to the study area for a future period 2011-2040.then, was evaluated by the coefficient of determination (R2) between observed and downscaled data.  A method has been used for the estimation of annual cumulative drought severity-time scale-frequency curves. According to the rainfall results, in the 2011- 2040 period rainfall would decrease  to compared to baseline period in the study area.

The SPI time series were estimated (2011-2040) and compared with the respective time series of the historical period 1961-1990. Results revealed that there are decreases in the frequency of severe and mild droughts for the three examined SPI time series while there are increases in the duration of moderate droughts. This implies that droughts will be a concern in the future during the growing season (for the dominant crop) which should be considered in water resources management. specially in the west station of Iran.

Also, these frequency ratios were mapped by GIS on study area. Results showed that generally in the future periods, frequency of droughts ratio of three months drought time- scale will be increase in the North, North West and some parts of the south Alborz mountains and, The Ratio of long ( 24 months) drought for scenario A2 compare to the current climate shows increasing drought in the parts of the North khorasn, sistan and baluchestan and kerman provinces and parts of South West of Iran. scenario A1B shows increasing drought in the parts of the East of Mazandaran , Tehran , Horozgan and parts of Fars and Yazd  provinces.

Finally ,further  more analysis of drought, AWCDS-Timescale-Return Periods computed. These curves integrate the drought severity and frequency for various types of drought. The AWCDS time series were estimated

for basic period and 2011-2040 under scenarios A2 and A1B. The comparison indicated the three types of drought intensity increases for the three examined SPI time series in the South East of Iran.

Drought is the most important natural disaster, due to its widespread and comprehensive short and long term consequences. Several meteorological drought indices have been offered to determine the features. These indices are generally calculated based on one or more climatic elements. Due to ease of calculation and use of available precipitation data, SPI index usually was calculated for any desired time scale and it’s known as one of the most appropriate indices for drought analysis, especially analysis of location. In connection time changes, most studies were largely based on an analysis of trends and changes in environment but today special attention is to the variability and spatial autocorrelation. In this study we tried to analyze drought zones in the North West of Iran, using the approach spatial analysis functions of spatial statistics and detecting spatial autocorrelation relationship, due to repeated droughts in North West of Iran and the involvement of this area in the natural disaster.

In this study, the study area is North West of Iran which includes the provinces of Ardebil, West Azerbaijan and East Azerbaijan. In this study, the 20-year average total monthly precipitation data (1995-2014) was used for 23 stations in the North West of Iran. In this study, to study SPI drought index, the annual precipitation data of considered stations were used. According to the statistical gaps in some studied meteorological stations, first considered statistics were completed. The correlation between the stations and linear regression model were used to reconstruct the statistical errors. Stations annual precipitation data for each month, were entered into Excel file for the under consideration separately and then these files were entered into Minitab software environment and the correlation between them was obtained to rebuild the statistical gaps. Using SPI values drought and wet period’s region were identified and zoning drought was done using ordinary kriging interpolation method with a variogram Gaussian model with the lowest RMS error. Using appropriate variogram, cells with dimensions of 5_×5km were extended to perform spatial analysis on the study area. With the establishment of spatial data in ARC GIS10.3 environment, Geostatistic Analyze redundant was used to Interpolation analysis Space and Global Moran's autocorrelation in GIS software and GeoDa was used to reveal the spatial relationships of variables.

The results showed that most studied stations are relatively well wet and this shows the accuracy of the results of the SPI index. Validation results of the various models revealed that Ordinary Kriging interpolation method with a variogram Gaussian model best explains the spatial distribution of drought in North West of Iran. So, using the above method the stations data interpolation related to SPI index in North West of Iran was done. The results showed that Moran index values for the analysis of results of standardized precipitation index (SPI) in all studied years, is more than 0.95. Since Moran’s obtained values are positive close to 1, it can be concluded that drought, in the North West of Iran during the statistical period has high spatial autocorrelation cluster pattern of 90, 95 and 99 percent. Results also showed that in all the years of study, Moran's global index is more than 0.95 percent. This type of distributed data suggests that spatial distribution patterns of drought in North West of Iran changes in multiple scales and distances from one distance to another and from scale to another and this result shows special space differences in different distances and scales in this region of the country. Results also showed that drought in North West of Iran in 2008 is composed of two parts: Moderate drought in parts of West and North West region (stations of Maku, Khoy, Salmas, Urmia, naghadeh, Mahabad and Piranshahr) and severe drought in the southeastern part of the study area (stations: Sarab, Khalkhal, Takab, Tabriz and Mianeh). So the pattern of cluster drought in the North West of Iran in 2008 is on the first and fourth quarter. The results of this index showed that drought and rain periods are similar in the studied stations. The results of the application of Moran's index about identifying spatial distribution of drought patterns showed that The values of the different years during the period,  have a positive a positive coefficient close to 1 (Moran's I> 0.959344) and this shows that the spatial distribution of drought is clustered. The results of the standard score Z values and the P-Value proved the clustering of spatial distribution of drought.

The results of the analysis of G public value, In order to ensure the existence of areas with clusters of high and low values showed that The stations of Maku, Khoy, Salmas, Urmia, naghadeh, Mahabad, Piranshahr and Parsabad follow the moderate drought pattern in the region and are significant at the 0.99 level. Jolfa station also has a mild drought of 0.95 percent confidence level and for Sardasht station is significant in 0.90 percent. High drought pattern in Sarab, Khalkhal, Takab, Tabriz and Mianeh stations was significant in 0.99 percent level and also for Ardabil, Sahand and Maragheh stations very high drought pattern was significant in 0.95 percent level and for Meshkinshahr and Ahar high drought pattern is significant in 0.90 percent. By detection of clusters of drought and rain in the North West of Iran using Moran’s spatial analysis technique and G general statistics a full recognition of the drought affected areas in this region can be obtained and take the necessary measures in its management 

Understanding the changes in extreme precipitation over a region is very important for adaptation strategies to climate change. One of the most important topics in this field is detection and attribution of climate change. Over the past two decades, there has been an increasing interest for scientists, engineers and policy makers to study about the effects of external forcing to the climatic variables and associated natural resources and human systems and whether such effects have surpassed the influence of the climate’s natural internal variability. The definitions used in the 5th assessment report were taken from the IPCC guidance paper on detection and attribution, and were stated as follows: “Detection of change is defined 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. An identified change is detected in observations if its likelihood of occurrence by chance due to internal variability alone is determined to be small.  Attribution is defined as the process of evaluating the relative contributions of multiple causal factors to a change or event with an assignment of statistical confidence”. Detection and attribution of human-induced climate change provide a formal tool to decipher the complex causes of climate change. In this study the optimal fingerprinting detection and attribution have been attempted to investigate the changes in the annual maximum of daily precipitation and the annual maximum of 5-day consecutive precipitation amount over the southwest of Iran.

This is achieved through the use of the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources Project(APHRODITE) dataset as observation, a climate model runs and the standard optimal fingerprint method. To evaluate the response of climate to external forcing and to estimate the internal variability of the climate system from pre-industrial runs, the Norwegian Climate Center’s Earth System Model- NorESM1-M was used. We used up scaling to remap both grid data of observations and simulations to a large pixel. This remapped pixel coverages the area of the southwest of Iran. The optimal finger printing method needs standardized values like probability index(PI) or anomalies as input data, since the magnitude of precipitation varied highly from one region to another. The General Extreme Value distribution (GEV) is used to convert time series of the Rx1day and Rx5day into corresponding time series of PI.  Then we calculated non-overlapping 5-year mean PI time series over the area study. In this research, we applied optimal fingerprinting method by using empirical orthogonal functions.  The implementation of optimal fingerprinting often involves projecting onto k leading EOFs in order to decrease the dimension of the data and improve the estimate of internal climate variability. A residual consistency test used to check if the estimated residuals in regression algorithm are consistent with the assumed internal climate variability. Indeed, as the covariance matrix of internal variability is assumed to be known in these statistical models, it is important to check whether the inferred residuals are consistent with it; such that they are a typical realization of such variability. If this test is passed, the overall statistical model can be considered suitable.

Results obtained for response to anthropogenic and natural forcing combined forcing (ALL) for Rx1day and Rx5day show that scaling factors are significantly greater than zero and consistent with unit. These results indicate that the simulated ALL response is consistent with Rx1day observed changes. Also, it is found that the changes in observed extreme precipitation during 1951-2005 lie outside the range that is expected from natural internal variability of climate alone and greenhouse gasses alone, based on NorESM1-M climate model. Such changes are consistent with those expected from anthropogenic forcing alone. The detection results are sensitive to EOFs. We estimate the anthropogenic and natural forcing combined attributable change in PI over 1951–2005 to be 1.64% [0.18%, 3.1%, >90% confidence interval] for RX1day and 2.5% [1%,4%] for RX5day.

Drought is one of the most important hazards that occur in all the earth especially in arid and semi-arid climates. Every year, about half of the earth’s surface experienced droughts and while drought is not a constant feature of any climate but occur more frequently in arid and semi-arid regions of the world. Although the occurrence of droughts cannot be prevented but by studying the nature and characteristics of droughts and also identify factors that affecting their occurrence useful information can be gained about drought and their destructive effects. The researches in recent years designed and proposed a lot of indices to study and analyze the droughts and today various characteristics such as intensity, duration, area and so on with these indices are studied. Many indices used by researches to analysis and identify properties of climatic droughts and dry periods. In these indices often the variables of precipitations, combination of precipitations and temperature, humidity or evaporation, crops yields and teleconnection climatic indices are used.

In this study using the CPEI index and 30 years (1980-2009) daily rainfall data in 40 synoptic stations overall Iran, to analysis and assess of Iran droughts suitable variables detected. Four seasons and annual period is considered in this study. To determine the appropriate variables in the design of suitable models and modeling of drought to assess and predict droughts Otun in 2005 proposed CPEI index as Conjunctive Precipitation Effectiveness Index. He selected 10 conjunctive precipitation variables as ORS(Onset of Rainy Season), CRS(Cessation of Rainy Season), LRS(Length of Rainy Season), TWD(The Total no of Wet Days), TDS(Total no of Dry Spell), TDW(Total no of Dry Days within a Wet Season), TDY(Total no of Dry Days within a Year), LDS(Length of the Dry Season), MDL(Maximum Dry Spell Length within a Wet Season), MAR(Mean Annual / Seasonal Rainfall Depth) and determined the relationships between variables in each synoptic stations and climatic regions. Since the units of measurement the rainfall variables are diverse, it is essential that the units be converted to a standard unit, in other words variables be standardized. The relationship between variables was determined by Pearson correlation coefficient. Finally, the right combination of precipitation variables for each station through the proposed formula Otun(2005) were determined. In the end, for each of the seasons and the annually period regionalization maps were prepared.

 All 40 synoptic stations were evaluated by Otun’s method (Aton, 2005). The results showed that 95 percent of stations in spring, 75 percent in fall, 57 percent in winter and 75 percent in annual period are compatible with used method. Thus, spring, fall and winter seasons and also annual period are compatible with above mentioned index. Among the used variables MAR, MDL, TDY and TDS which with respectively are as follows: total amount of precipitation in any period, the maximum duration of dry periods in a wet period, the total number of dry days in a wet period and the total number of dry period during wet period among the stations are more abundant. In annually period, in addition to the above mentioned variables, precipitation variable of LPS (length of dry period) also seen among some stations. Also, results showed that CPEI index can be used on most stations and climatic regions of Iran. It was also found that the spring compared the other seasons and annual period is more comparable on the base of CPEI index.   

  Otun in 2010 used the CPEI index in semi-arid region of Nigeria and has achieved good results. The results of our study show good agreement with Otun’s work. The use of this index in the study of meteorology, climatology, agriculture and many environmental projects can be beneficial because in many of these fields of study, precipitation and its characteristics have an important role. In general we can say that in regions where CPEI index does not show a high proportion or set of variables are not enough it is better to use other indices such as SPI and RAI. The results obtained in similar climate zones such as Nigeria has shown that CPEI index has very good ability to identify and explain the precipitation effectiveness variables which can be used in modeling of droughts and dry periods. There are many similarities between combination of precipitation variables that identified by CPEI index for Iran and other regions of the world. Similarities, especially with respect to MAR, MDL, TDY and TDS are abundant.

Climate change is one of the crucial factors, which threaten many sector such as agriculture, water resource for decades, and the sector is more sensitive to climatic conditions.  Communities are the most vulnerable to the adverse impacts of climate change and variability because of their low adaptive capacity. One of the challenges of climate change and human spatial dimensions of climate change in international borders where climate change, and creates special challenges. Populated places in the East where rapid urbanization, industrialization and agricultural intensification result in vulnerability to climate change, water shortages as the main concern arises.

 Adaptation to climate change is the adjustment of a natural or human system to moderate the impacts of climate change, to take advantage of new opportunities or to cope with the consequences. Trying to identify the attitudes of people and their mental models of climate change can provide application to manage the post-change. Culture and engineering modeling approaches minds of scientists for climate risk management and climate change consequences have adopted. The review focused on farmers’ perceptions on changes in temperature, precipitation (rainfall), adaptation measures taken by farmers, barriers inhibiting these adaptation measures and the socioeconomic determinants of adaptations to climate change in Sistan plain.

The aim of this study is to provide mental system model, and understanding of climate change is to adapt these areas. To carry out this study to develop a theoretical framework for the model to adapt to climate change was discussed in Helmand. The field study was to assess the views of people on climate change action. The review found out that most farmers in this region are aware that the continent is getting warmer, and precipitation or rainfall patterns have changed. People with new changes and features adaptive approach to the challenges ahead were investigated. This data is based on knowledge (awareness) of water and climate change adaptation and mitigation strategies and be ready. So how compliance action is preventive in nature and to reduce the repercussions of climate change and the potential benefits of a region in the face of these side effects are formed. Most respondents aged over twenty years are at least a decade to climate change are felt to be most frequent subjects 30 to 40 years old. The data collected were processed using statistical techniques and modeling for ranking and evaluation of adaptation strategies were created and ASI index. The results for the insights, policy makers and service providers for local development is important, and can be targeted measures used and the promotion and adoption of coping mechanisms with the potential to build resilience and adapt to climate change and the resulting effects environmental prepare.

The results showed that most people in the region following the election of climate change is adaptive behavior. In total, there are 15 strategies in the region. The ASI index rating of strategies to change the pattern of cultivation, selection of resistant strains, reducing the amount of land-cultivated variety is the pattern of adaptation to environmental changes. Ensuring awareness of and adaptation to climate variability call was conducted with the cooperation of the people. Therefore, variability of climate and natural features of the area was measured by various options. The results show that already sampled respondents in the community are aware of climate change. 60% of respondents strongly observed signs of climate change and the dry season and low rainfall and warmer temperatures to believe. The main adjustment options adopted by farmers to temperature in the region include change of product types and number of ships that 61.6 percent of the farmers that their efforts. Another priority is that 39 percent of them tend to change sowing dates and planting varieties resistant to drought. The main recommendations for adapting to new circumstances in this region to stimulate the economy and livelihood of local people can be to diversify crop production (food for example, and cash crops, annual and permanent crops greenhouse) and the use of foreign income from farm sources (ecotourism, rural tourism) can be cited.

Climate risks are the inherent features of Earth's climate. The occurence of heat wave is one of these natural phenomena. Heat waves, one of the basic appearances of climate change, are very important because of frequency and damage of life and property, (Haddow et al, 2008). Frequency of heat wave occurence in recent years, is one of the aspects of climatic changes and extreme weather (Matthies et al, 2008), and resulted in heavy financial loss and increasing p mortality. From statistical point of view, heat waves are the positive changes and upper extremes of maximum average daily temperature, which continuing during consecutive days, weeks or months in certain geographical areas. According to the available definitions, two dimensions of time and space are important in the occurrence or non-occurrence of heat waves  (Smith,2013). Due to the positive slope of temperature and increase in temperature extremes and many changes in values of maximum temperature in Iran, main purpose of this study is the spatial and time distribution of heat waves on the plateau of Iran.

The daily maximum temperatures recorded in 49 synoptic stations of 31 years (1980-2010) climate normal period were used for the spatial distribution of heat waves. In order to determine heat waves, using the 95th percentile index, the temperature threshold for each month and each station was determined separately. The reason of studying heat waves in the monthly scale is temperature differences and different consequences in different parts of Iran, as an example, maximum temperature 30 degrees in May for south of Iran is normal, but for the northern regions of Iran is a heat wave and causes damage. So the basis in this study is determining heat waves and spatial differences of these phenomena in monthly scale. In this study, the heat wave has been defined as temperatures above the 95th percentile threshold per month, continuing for three days and more. So with specifying the threshold temperature for each month at each station in different parts of the country, temperatures above the threshold continuing for three days and more, defined as a heat wave for each month and the spatial distribution of heat waves was plotted in the whole area of Iran plateau for each month. In order to determine changes in heat waves in the whole country, the number of heat waves has been specified for the whole country in three decades (80-90-2000).

The spatial distribution of heat waves: Maximum temperature thresholds are related to the southeastern, southwestern and southern stations; and the lowest thresholds are northern coast and northwest mountains stations. In general, the minimum temperature thresholds are visible in the northern half and towards the heights; however, the maximum thresholds are visible in southern half. In this temperature variable, the role of latitude and altitude is dominant in lines with the same threshold of extreme temperature like other temperatures properties in Iran. Spatial variations of this temperature parameter throughout the year, increased from the Caspian Sea and North West of Iran to the South East and South West of Iran. In the entire study period, the number of heat waves in different parts of Iran indicates that most heat waves were occurred in the mountainous regions of Iranbased on the zoning temperature Alijani. The number of heat waves decreased from this area to the north and south coastal areas and East of and Central of Caspian has the lowest number of heat waves during the entire period of the study in Iran.

Time, temporal and decade distribution of heat waves: Time changes in heat waves shows increasing trend, As we can see the increase in the number of heat waves, from mid-90s and then, in 2010 most of it.Also, the 5-year average and decade-long average of heat waves, show a significant increasing trends and the most of the heat waves occur in Iran during 2000s. Time series of heat waves in Iran; show a significant increase over time.Hence, from the late 90's onwards, the spatial average of heat waves rather than the average before these years has increased. Iranian plateau in 1992 and 2010 has experienced the minimum and maximum of heat waves, respectively.

The results showed the minimum temperature threshold along the heights in northern half of the country and maximum temperature threshold at the southern half. Spatial variations of this thermal parameter throughout the year, is increased from the Caspian Sea coast and the North West of Iran toward the South East and the South West of the country. In general, this parameter that is associated with the extreme temperatures in Iran is under latitude and heights distributions the same as distribution of maximum temperature areas in Iran. But spatial distribution of heat waves as a natural hazard is different from the distribution thresholds and maximum temperatures. So that, the most heat waves are in Zagros Mountains, the East foothills of Zagros, South of Western and central Alborz and also southern Binalud foothills in the North East. The number of heat waves is reduced toward the center of Iran and the Great Plains (Lut and Kavir deserts). The minimum heat waves occur on the coasts of Caspian Sea, southern coasts of Iran, South-West and West Zagros and central Iran. The occurrence of heat waves in Iran have an average between 9 and 14 heat waves during all months of the year except for May with a maximum of 6 heat waves and June, with a maximum of 16 heat waves (months of minimum and maximum occurrence, respectively). This shows minimum increase in cold months and maximum increase in warm months. Therefore, the occurrence of heat waves in Iran is possible in warm and cold periods of whole year and there is a little difference between these two periods. This indicates both internal (local) and external factors (air masses) involved in occurrence of heat waves in Iran. The number of heat waves increase and decrease since January and June, respectively. This temporal sequence is disrupted by a sharp decrease in May (6 heat waves less than previous month).