Journal of Spatial Analysis

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Global changes in extremes of the climatic variables that have been observed in recent decades can only be accounted anthropogenic, as well as natural changes. Factors are considered, and under enhanced greenhouse gas forcing the frequency of some of these extreme events is likely to change (IPCC, 2007 Alexander et al., 2007). Folland et al. (2001) showed that in some regions both temperature and precipitation extremes have already shown amplified responses to changes in mean values. Extreme climatic events, such as heat waves, floods and droughts, can have strong impact on society and ecosystems and are thus important to study (Moberg and Jones, 2005). Climate change is characterized by variations of climatic variables both in mean and extremes values, as well as in the shape of their statistical distribution (Toreti and Desiato, 2008) and knowledge of climate extremes is important for everyday life and plays a critical role in the development and in the management of emergency situations. Studying climate change using climate extremes is rather complex, and can be tackled using a set of suitable indices describing the extremes of the climatic variables.    The Expert Team on climate change detection, monitoring and indices, sponsored by WMO (World Meteorological Organization) Commission for Climatology (CCL) and the Climate Variability and Predictability project (CLIVAR), an international research program started in 1995 in the framework of the World Climate Research Programme, has developed a set of indices (Peterson et al., 2001) that represents a common guideline for regional analysis of climate.    It is widely conceived that with the increase of temperature, the water cycling process will be accelerated, which will possibly result in the increase of precipitation amount and intensity. Wang et al. (2008), show that many outputs from Global Climate Models (GCMs) indicate the possibility of substantial increases in the frequency and magnitude of extreme daily precipitation.     eneral circulation models (GCMs) are three-dimensional mathematical models based on principles of fluid dynamics, thermodynamics and radiative heat transfer. These are easily capable of simulating or forecasting present-future values of various climatic parameters. Output of GCMs can be used to analyze Extreme climate. For this study high quality time series data of key climate variables (daily rainfall totals and Maximum and minimum temperature) of 27 Synoptic stations were used across Iran from a network of meteorological stations in the country. In order to get a downscaled time series using a weather generator (LARS-WG), the daily precipitation output of HadCM3 GCM, SRES A2 and A1B scenario for 2011-2040 are estimated.     The Nine selected precipitation indices of ETCCDMI[1] core climate indices are used to assess changes in precipitation extremes and monitor their trends in Iran in the standard-normal period 1961–1990 and future (2011-2030).    Due to the purpose of this study, at first changes in extreme precipitation indices in the standard-normal period is evaluated and its results show annual maximum 1-day precipitation increased in many regions in the East of Iran. Simple measure of daily rainfall intensity (SDII), annual maximum consecutive 5-day precipitation, annual count of days with daily precipitation greater than 10mm (R10mm), annual count of days when rainfall is equal to or greater than 20 mm (R20mm) have increased in the central areas, regions in the north , north east and southern parts of Iran. Similar results are obtained for the R25mm index.    The consecutive dry days (CDD) index has generally increased across the west areas, southwest, north, northwest and southeast of Iran and indices of consecutive wet days (CWD) decreased in these areas.    Trends of extreme precipitation indices simulated by HadCM3 SRES A2 showing increases RX1Day in North West expect west Azerbaijan Province, central, southwest, north east and coasts of Caspian Sea. Similar results are obtained for the R5mm index expects northeast. There are mixed changes in R10mm across Iran, increasing in west, southwest, coasts of Caspian Sea, Hormozgan and Ardebil provinces, East Azerbaijan, Zanjan and Qazvin  provinces. Similar results are obtained for the R20, 25 mm index in northeast, south of Caspian Sea, and some parts in western and central areas. Same as HadCM3 SRES A2 pattern there are mixed changes in R10mm across the region. Positive trends are seen in part of the Isfahan, Markazi, Kuhkilue , Lorestan, Ilam, Chaharmahaland Khozestan provinces and some part of Hormozgan and Kerman and some areas in north west. Similar results are obtained for the R20mm and R25mm index and in west of Yazd to north of Khozestan provinces have increased.    Consecutive wet days (CWD) have increased over most of the west of Iran, Khorasn Razavi and Southern Khorasn provinces, In contrast consecutive dry days (CDD) index has generally increased in many parts of the region.  
[1]. Expert Team on Climate Change Detection and Monitoring Indices

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Tehran, in the south of Alborz Mountains, is faced with three types of weather risk, weather risk caused by geography, climatic risks caused by air resistance and weather risk due to global warming. The aim of this study is to examine the three types of risk in Tehran. The method of this study was to evaluate the changes of synoptic factors that affect global warming and urban development. In order to detect the height changes of 500 hPa two 5-year periods including 1948 to 1952 and 2010 to 2014, were studied.

     The results showed that changes in heights of 500 geopotential, there was an increased risk in the city of Tehran. The effect of climate change in recent decades,  increased the stability of  air in Tehran. Human factors in the formation of heat islands, increase LCL height and density of the air balance is transferred to a higher altitude. Changing urban wind field, atmospheric turbulence intensified, exacerbated thermodynamic gradient, fat and refugee cyclones, heat island effect of the city.

Thermal stability in the warm period will appear. The thermal stability of all levels of lower, middle and upper troposphere was intensified. Thermal stability couraged the  development of subtropical high pressure in the area. With the arrival of the atmospheric pressure during calm and humid days the stability and pollution were increased. Negative vorticity from early June  developed the intensive high pressure over the region. Compare the conditions of the two study periods  showed that  : the height of the high pressure was 100 meters higher than the second period. The number of days of intensified subtropical high increased during the second period.  The high pressure has moved to the northern areas during the second period. This change in the subtropical high pressure increased the dry periods motivating the loss of vegetation. Heat island effect was increased as well. More than 90% of the  temperature inversions occurred  at an altitude of less than 500 meters in both warm and cold periods of year. Wind direction at both stations has shown that the establishment of any pollutant source in the West of Tehran will increase the pollution.

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Climate change is one of the most significant threats facing the world today. One of the most important consequences of climate change is increasing frequency of climate hazards, mainly heat waves. This phenomena has a robust impacts on human and other ecosystems. The aim of this study is investigating changes of heat waves in historical (1980-2014) and projected (2040-2074) data in northern cost of Persian Gulf.

The focus here is on Mean daily maximum temperature and Fujibe index to extract heat waves. For this purpose 6 weather stations locating in north coast of Persian Gulf, Iran, are used (table 1).

Table1: weather stations

Station	Latitude	Longitude	Elevation(m)
Abadan	30° 22' N	48° 20' E	6.6
Boushehr	28° 55' N	50° 55' E	9
Bandarabbas	27° 15' N	56° 15' E	9.8
Bandarlengeh	26° 35' N	54° 58' E	22.7
Kish	26° 54' N	53° 54' E	30

  In addition, 4 model ensemble outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are used to project future occurrence and severity of heat waves (2040 to 2070), under Representative Concentration Pathways 8.5 (RCP8.5), adopted by the Intergovernmental Panel on Climate Change for its Fifth Assessment Report (AR5) (table 2).

Table2: List of the AR5 CMIP5 Used Models

Model	Modeling Cener	Country
CanESM2	Canadian Earth System Model	Canada
MPI-ESM-MR	Max-Planck-Institut für Meteorologie	Germany
CSIRO-Mk3-6-0	Commonwealth Scientific and Industrial Research Organization	Australia
CMCC-CESM	CMCC Carbon Earth System Model	Italy

The output of models is downscaled using artificial neural network method (ANN). A feed-forward network of multi-layer perceptron with an input layer, a hidden layer and an output layer is used for this purpose. 73 percent (1980 – 2000) of the data is used for training and 27 percent (2000-2005) for testing ANN models. Root Mean Square Error (RMSE) is used as an indicator of the accuracy of Models.

RMSE=

Here   is the outputs of ANN models (downscaled data) and is the observation data.

Fujibe et all (2007) used an index based on Normalized Thermal Deviation (NTD) for extracting long-term changes of temperature extremes and day to day variability using following equations:

Where N is the number of days in the summation except missing values. Then nine-day running average was applied three times in order to filter out day-to-day irregularities.

=(i,j,n)=T(i,j,n)-T(I,j)

The departure from the climatic mean is given by

=

If NTD >2 and at least lasts for 2 days it determine as a heat wave.

Results

Table 3 shows the results of downscaling selected GCM models.

	nodes		RMSE					Average RMSE
	Sigmoid function	Linear function	Abadan	Bushehr	Bandarabbas	Bandar-e-Lengeh	Kish
CanESM2	5	1	9.6	6.1	4.85	4.7	4.5	5.97
MPI-ESM-MR	5	1	9.3	7.1	3.9	5	4.3	5.9
CSIRO-MK3-6-0	15	1	8.8	5.6	3.6	3.4	3.6	5
CMCC-CESM	10	1	9.2	5.8	3.9	4.7	3.9	5.5

Table 4 compares the frequency of heat waves for GCMs and historical data.

	CanESM2	MPI-ESM-MR	CSIRO-Mk3-6-0	CMCC-CESM	Historical data
Abadan	434	401	448	387	430
Bushehr	376	423	420	406	407
Bandarabbas	441	405	457	382	410
Bandar-e-Lengeh	380	414	388	401	400
Kish	421	442	415	442	399

For historical data, heat waves are more frequent in Abadan station than other stations. There is an increasing trend in the occurrence of heat waves in historical data and monthly frequency of heat waves show the highest amounts for summer.

For both historical and future data 2 days listening heat waves are more frequent.

Table 5 shows seasonal changes of heat waves for historical data and GCMs.

	season	The ratio of heat waves from total historical data (percent)	The ratio of heat waves from total projected data (percent)
Abadan	Spring	30.43	24.02
	Summer	29.19	27.87
	Autumn	17.39	22.61
	Winter	22.98	25.48
Bushehr	Spring	21.42	24.23
	Summer	25	26.21
	Autumn	28.57	24.82
	Winter	24	25.32
Bandarabbas	Spring	21.73	24.7
	Summer	26.81	27.01
	Autumn	25.81	25.17
	Winter	24.1	24.63
Bandar-e-Lengeh	Spring	23.55	23.74
	Summer	23.33	29.82
	Autumn	23.74	25.81
	Winter	25.17	20.8
Kish	Spring	24.27	24.8
	Summer	25.53	28.32
	Autumn	23.35	25.21
	Winter	23.1	23.8

In recent years the frequency of heat waves is increasing in all studied stations. Coincide with Russia and Europe, the highest amounts of heat waves is occurred in 2010 in northern coast of Persian Gulf and this is adopted Esmaeilnezhad et all (2013), Gavidel (2015) and Azizi (2011).

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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.

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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.

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Farmers frequently cope with risks due to the uncertainty of climatic conditions. Population growth, changes in agricultural policies, environmental regulations and the degradation of natural resources such as soil and water also present farmers with numerous challenges. Although farmers have experience in coping with a certain degree of uncertainty, increased climate variability and changes may cause severe problems. Drought in particular is a climatic disaster that creates substantial costs for farmers and affects their agricultural systems extensively. Drought is the most complex of all natural hazards, making the arid and semi-arid regions of the world vulnerable. Although drought has not been well documented, the resource-dependent sectors such as agriculture are the most vulnerable to the impact of this phenomenon. A review of the long-term annual precipitation trends indicated that drought had a worldwide return frequency of every 20e30 years. However, in the last 50 years, some countries such as Iran and Bangladesh have experienced approximately 27 and 19 drought events, respectively. Therefore, for arid and semiarid regions, drought is a recurrent feature that could lead to the loss of crop production, food shortages and starvation) if not managed appropriately. According drought impacts could be managed at macro (national), meso (local) and micro (village and household) levels. However, the micro-level management (i.e., what the farmers do in response to drought) is of great importance. A review of the studies of farmers’ decision-making in response to climate variability revealed that most research has focused on the decision event and not on the entire process argued that the wrong assumption of farmers’ homogeneity neglected different aspects of decision-making in response to drought. Also indicated that farmers made different decisions when utilizing the same data. Additionally, many studies have focused on single strategies that were used to mitigate drought. However, there is a lack of knowledge about the combination and sequence of coping strategies that are used to mitigate drought. Concentrating on the decision-making process could help policy makers assess the needs and prioritize interventions, as well as enable farmers to efficiently manage drought. Farmers utilize various strategies to reduce the impacts of drought. Some strategies have a limited impact on drought mitigation. Some practices also increase farmers’ woes during drought. In addition, when resources (natural, physical and financial) are scarce, the need for an accurate appraisal of coping strategies becomes acute. Therefore, outcome prediction (i.e., the efficacy of mixed coping strategies) is a critical issue in drought management. Consequently, this study is concerned with the description of the farmers’ decision-making process and decision outcomes. First, the impacts of drought on the agricultural production in arid or semi-arid countries, specifically Iran, are described. Then, the farmers’ decision-making process during drought is explained then, the farmers’ decision-making process during drought is explained. The focus then shifts to the design and explanation of the proposed research methodology, followed by an analysis of the results and concluding remarks. Approximately $84 million. Under such conditions, Iran imported significant amounts of wheat and rice, and it seemed likely that continuous drought would lead to import expansion. Furthermore, dairy production also experienced a decrease of 8.2 percent during this same period. The drought of 2008e2012 was one of the worst on record. This drought drastically reduced the cultivation area, even in irrigated lands. During this time, the river waters fell to critical levels. Most of the traditional ground water irrigation systems (qanats) either completely dried up or experienced a reduced water release. In the central and southern regions of Iran, the cultivation areas were reduced by half during the spring-summer seasons due to these low water levels. During this period, farmers experienced rising costs due to the use of management strategies such as deepening wells and constructing water storage in order to cope with the drought. Other economic impacts that were experienced by the farmers were increased livestock feeding expenses, increased interest rates, and increased debts. These depleted resources and diminished incomes forced those in rural areas to migrate to the cities in pursuit of jobs. Important factors, as previously mentioned, are livelihood risks that so far have not been given much attention so this research was to Patterns Design Out of the Challenges of Livelihood Sustainability of Small-Scale Farmers in Drought Conditions in Kurdistan Province.
The statistical population consisted of small farmers in Kurdistan province who were in drought conditions. The research paradigm is qualitative in two ways: Grounded theory and phenomenology.  Using theoretical sampling, 29 of them were selected for study. The research data were collected using a deep interview and group discussion and analyzed with three open, axial and selective coding methods.
The results of the research in the phenomenology of Livelihood Behavior Behaviors included 16 primary codes and classified into adaptive behaviors, resiliency and non-response. Also, the results of studying the livelihood sustainability challenges of small scale farmers in the form of foundation data methodology included 61 initial codes. Finally, in order to design a model out of the challenges of the stabilization of 9 mechanisms (economic, productivity, production factors, services and facilities, Education and information, management and capacity building, culture, technology, formations, and equilibrium) were designed based on the challenges of sustainability and incorporated into the Strauss model. 
 
Keyword: Sustainability, Sustainable Livelihoods, Climate Risks, Small Scale -Farming
 
 
 

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Climate change stand as the most important challenge in the future. Horticulture is one of the most sensitive and vulnerable sectors to the climate change. Climate change and global warming will endanger the production of agricultural products and food security. Because of required longer time to fruit production, fruit trees are heavily susceptible to damage from climate change. The purpose of this study was to investigate the impacts of climate change on thermal accumulation pattern in Apple tree cultivation regions of Iran based on the outputs of new CMIP5 models and radiative forcing (RCP) scenarios.
The present study was carried out using a statistical-analytical method. In this study, two types of data was used; baseline data for past period and model output simulation data for the future period. Observation data for baseline period for 53 weather station was extracted from the Iran meteorological organization (IMO). Afterwards, the data for the upcoming period up to the 2090 horizon were processed using the HadGEM2-ES model from the series of CMIP5 models of the MarksimGCM database based on the radiative forcing scenarios RCP8.5 and RCP4.5. The future period will be refined in the mid-term (2020-2055) and the far future (2056-2090). Afterwards, based on the thermal thresholds, thermal accumulation in Apple tree cultivation areas in Iran processed.
The results showed that based on statistical indices on the output of CMIP5 models, the output of the HadGEM2.ES general circulation model is accompanied by fewer simulation errors in illustrating the climate change of the future period than the observation or baseline period. In fact, based on the evaluation criteria or error measures, this model shows a higher compliance with observational data. In general, the model has a lower accuracy than precipitation in the simulation of rainfall, which is due to the complexity of the precipitation process as well as the structure of the climatic models. One of the fundamental issues that have emerged in recent decades is the change in the potential status or heat accumulation of different regions due to the increase in air temperature. The results showed that due to temperature increase, in the mid and far future heat accumulation will increase compared to the baseline period in Apple tree cultivation areas. Increasing of heat accumulation will reduce the length of the Apple tree growth period, and in fact the Apple tree will complete its vegetative and reproductive cycles sooner. This condition will have negative effects on the quality, taste and color of the Apple varieties. For example, according to the RCP8.5 scenario in the physiological threshold of the apple tree 4.5 C^° , in the mid term (2020-2055) and far future (2056-2090) will be 1132 and 2171 active degree days respectively compared baseline period. These conditions equivalent to the  51% and 42% respectively. Based on the RCP4.5 scenario, these conditions will be 390 and 680 active degree day, equivalent to 9.3% and 15.1%, respectively, compared to the baseline period.
The results showed that the heat accumulation in Apple tree cultivation areas in the future period will increase compared to the baseline period. One of the most important effects of climate change on the Apple tree  cultivation will be due to increased heat accumulation in the upcoming period. Increasing the heat accumulation will reduce the length of fruit tree growth period, and in fact the fruit tree will complete its vegetative and reproductive cycles earlier. According to these conditions, the areas of Apple tree cultivation in the future will be extended to higher regions. These conditions are important for cold regios fruit tree such as Apple tree, in facr increase in heat accumulation will reduce the length of the growing season and, as a result, reduce the quality and yield of the fruit. Based on the spatial distribution, the least heat accumulation in the highlands, especially Northwest and central Alborz, will occured. In natural landscapes of low elevations, valleys and plains in the Northeast, central Southern part of the Zagros and around Lake Urmia, higher heat accumulation will occured in the future. Therefore, one of the effects of climate change on fruit trees will be due to increased heat accumulation in the upcoming period. Increasing the potential or heat accumulation will reduce the growth period of the fruit trees, in fact, the fruit trees will complete their vegetative and reproductive cycles sooner.
 
 
 

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Introduction
Atmospheric boundary layer (ABL), is the lowest part of the atmosphere. Its behavior is directly influenced by its contact with earth surface. On earth it usually responds to changes in surface radiative forcing in an hour or less. In this layer physical quantities such as flow velocity, temperature, moisture, etc., display rapid fluctuations (turbulence) and vertical mixing is strong. Above the ABL is the "free atmosphere" where the wind is approximately geostrophic  while within the ABL the wind is affected by surface drag and turns across the isobars. The land use/cover changes affecting the surface radiative forces lead to ABL spatio-temporal variation. The main object of this study is to analysis the association among ABL height and built-up spatial growth in Kermanshah city.  
Data and methods
Multi-temporal satellite images from Landsat imagery data for 1990 to 2015 series of sensors TM, and OLI (Landsat 5 and 8) were taken from USGS database. Data of the Atmospheric Boundary layer height (ABL height) for the city of Kermanshah also were taken during 1990- 2015 from ECMWF – Eran-Intrim website at 0.0125 ° spatial resolution. Firstly, we analysis the temporal trends of ABL height of Kermanshah in summer and winter using linear regression in 0.95 confidence level (P_value = 0.05). The built up area of Kermanshah has been extracted from TM and OLI images using supervised classification method and maximum likelihood classification(MLC) algorithm in GIS image analysis. The Pearson correlation analysis has been used to reveal the relationship between annual ABL height variation and built-up growth of Kermanshah.
 Result
The results of long term trend of Built up growth of Kermanshah that extracted using MLC algorithm as can be seen in figure 1 indicated that the built up area in Kermanshah has been growth by 1.02 square kilometer annually.According the figure 2, The results of annual trend of ABL height in summer and winter also reveals that in summer there is no significant trends in ABL height while in winter the significant increasing long term trend has been observed in ABL height.  

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Global temperatures have increased in the past 100 years by an average of 0.74°C (IPCC, 2013), with minimum temperatures increasing faster than maximum temperatures and winter temperatures increasing faster than summer temperatures (IPCC, 2013). Total annual rainfall tends to increase at the higher latitudes and near the equator, while rainfall in the sub-tropics is likely to decline and become more variable (Asseng et al., 2016). Considering probability of occurrence climate change and its hazardous impacts, it seems essential to clarify future climate. General Circulation Models is widely used to assess future climate and its probable changes. Although the outputs of these models are not appropriate for small-scale regions because of its coarse resolution. Thus, statistical or dynamical techniques are used to downscaling the outputs of these models using observed data in weather stations. Despite the fact that frequent researches has done in relation with climate and climate change, but it is unclear yet future climate, especially climate change, in Iran. The goal of this study was to present the results of climate change predictions which has been done so far in Iran, in order to help prospective studies in this field. This step can be important to consider new questions and challenges. In this study, we assessed future climate change in Iran using results of statistical downscaling studies of atmospheric-oceanic General Circulation Model’s outputs. To do this, studies on prediction of precipitation and temperature parameters in Iran by different emission scenarios, atmospheric-oceanic General Circulation Model’s outputs and statistical downscaling techniques were gathered. Then a comprehensive view about Iran's future climate and specifically the climate changes presented by descriptive-content based analysis and comparison of their results. Used downscaling techniques in these researches were included: LARS-WG, SDSM, ASD, Clim-Gen and used General Circulation Models were: HADCM3, BCM2, IPCM4, MIHR, CGCM3, CCSM4 and finally used emission scenarios were A1B, A1, A2, B1, B2, RCP4.5. Based on climatically geographical differences in Iran, the results discussed separately in six different regions across Iran. The results of various regions are different because of usage of different models and different climatological and geographical conditions. These models simulate temperature more accurate than precipitation, because of more variability and temporal discontinuity of the precipitation relative to temperature. Assessment of results in 30-year periods from 2011 to 2099 showed that in North West of Iran (Ardebil, Azarbayejan- Sharqi and Azarbayejan- Qarbi provinces), precipitation will be decreasing, decreasing- oscillating, decreasing- transitional and temperature will be increasing. Decreasing- transitional trend, in other words decrease precipitation in cold seasons and increase of it in warm seasons, lead to a decrease in the snow occurrence and an increase in the rainfall occurrence. Thus, it can affect the frequency of floods occurrence. In west and southwest region of Iran precipitation has been predicted to have different changes in various sections of it. It will be decreasing-oscillating in Kermanshah and Kordestan provinces and oscillating in Hamedan province. Precipitation will increase in Lorestan and finally it expected to decrease in Khoozestan, Chaharmahal-va-Bakhtiari, and Ilam. However Temperature will rise across this region. In south and south east region of Iran (Fars, Hormozgan, Kerman and sistan-va-Baloochestan provinces), precipitation will be decreasing, decreasing-oscillating, oscillating and increasing-oscillating. Also in this region, temperature expected to increase similar to other regions. In east and north‌ east of Iran (Khorasan Shomali, Khorasan Razavi and Khorasan Jonobi provinces), temperature predicted to be increasing-oscillating, that it is different with other regions. Changes in precipitation will be oscillating and decreasing-oscillating. In the northern coasts of Iran (Gilan, Mazandaran and Golestan provinces), precipitation changes will be decreasing and increasing-oscillating and temperature changes expected to be increasing and increasing-oscillating. Thus, it expected to increase heat wave, drought, and aridness condition as the results of these changes. Precipitation changes in south of Alborz region and center of Iran (Semnan, Tehran, Qazvin, Markazi, Esfahan and Yazd provinces), will be decreasing, oscillating, increasing-oscillating. Also temperature will be increasing in this region. Considering the decreasing trend of precipitation and the increasing trend of temperature in the most of Iran, it is probable to increase the occurrence of climatic and environmental hazards such as flood, drought and heat waves in the future. These events can have serious effects on water resources, agriculture and tourism, especially in regions such as Iran where have sensitive environment.

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 Extended  Abstract
Cold and frost is one of the most important climatic parameters in the agricultural climate, and the damage caused by them reduces the possibility of producing many agricultural and horticultural products in vulnerable areas. Cold and frost is one of the climatic hazards that annually causes damage to various activities. The agricultural sector is the most important part of the damage that is most seriously damaged by frost. Cold and frosty weather for many crops and gardens results in harmful and destructive consequences, in some years billions of rials damage farmers, farmers and, ultimately, the national interests of the country. Considering that the northwest region of Iran suffers a lot of financial losses each year due to atmospheric hazards especially cold and frost. Identification and zoning of areas with high potential of cold and frost hazard and prediction of their occurrence can provide valuable and valuable information for preventing and mitigating damages. In this study using HadCM3 global model under two scenarios A2 and B1 and The LARS-WG microscope model is dealt with this.
It is important to check the time of occurrence and predict their future changes. For this purpose, general atmospheric circulation (GCM) models are designed that can simulate future climate parameters. In this study, the output data of the HadCM3 general circulation model under two scenarios of A2 and B1 were analyzed by LARS-WG statistical method in 21 synoptic stations located in northwest of Iran. The results of this study were based on the base period (1980-1989) and The 2020 decade (2030-2011) was evaluated for two climate variables: minimum temperature and maximum temperature. Then the history of the first and last frost and cold of autumn and spring was extracted and their date of occurrence was calculated in the future.
The monthly average of the minimum temperature of the stations studied in the course of the 2020s and the base period shows that the temperature has been increased according to both scenarios and increased in all months and at most study stations compared to the base period. The maximum changes in the minimum temperature in the study area are based on the average scenarios in this decade related to Abhar, Ardebil, Khoy and Urumieh stations at 0.8 degrees Celsius; In fact, the minimum temperatures that occurred at these stations during the base period have not been observed in the next period and the heating process has shown that its rate in the region of the study area in the 2020s is between 0.4 and 0.8 It will be in the base period. The results indicate an increase in the monthly average of the minimum and maximum daily temperatures in the upcoming period to about 0.8 degrees Celsius. The results of the first and last glacial survey in the decade of 2020 indicate that the first glacial precipitate of autumn occurs between 2 and 9 days later, with the least change in the history of frost occurring in two stations of Qazvin and Meshkinshahr each with 2 The change day is relative to the base period. The last frost of late spring also will be 3-10 days earlier on the surface of the region. However, the duration of the ice free period will be reduced at all stations, which is the highest decrease for Khoy station with 16 days, then the stations of Urmia and Ardebil each Two with 14 days and the lowest decrease is due to Meshkinshahr station for 6 days. Based on the results of changes in the date of early ice ages, changes are less than the late frost. Based on this, the study of the condition of glaciers and serma in most of the studied stations shows that the first frost and autumn frost in the coming period will start earlier and the cold and the frostbite will end sooner. The least changes were observed in the south-east of the study area, Meshkinshahr and Sarab regions, and the most changes in the glacial period were related to Khoy, Urmia, Tabriz and Ahar areas. According to the results of most studied areas, averaging between 10 and 12 days decrease in length The ice age will experience the base course.
The results indicate an increase in the monthly average of the minimum and maximum daily temperatures in the upcoming period to about 0.8 C. Based on this, the study of the condition of glaciers and serma in most of the studied stations shows that the first frost and autumn frost in the coming period will start earlier and the cold and the frostbite will end sooner. Also, the length of the cold and freezing period is decreasing, which may reflect the consequences of climate change at study stations. The results of this study are based on the studies of Grasick and Dodwilich (2015) in Poland, Medella et al. (2016) in Texas, Hosseini and Ahmadi (1395) in Saqez, Aqa Shariatmadari et al. (1395) in West Iran, Sobhani et al. (1396). ) In Ardebil and Khalili et al. (1396) in Iran.
 

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Abstract

Climate change in one area has severe impacts on water resources and, consequently, agriculture in that area. Therefore, studying the extent of the vulnerability of regions to adopting policies to reduce or adapt to new conditions is of particular importance. One of the methods for assessing the extent of damage to agricultural activities is the calculation of the vulnerability index. In this study, with the aim of assessing agricultural vulnerability to climate change, The CVI index was calculated for 16 cities in Guilan province.

The results showed that the cities of Rasht (61.58) and Talesh (55.21) had the highest vulnerability and, accordingly, had the least adaptive power to climate change compared to other cities. And Langrood County (29.51) has the lowest number of vulnerabilities. The average value of the calculated index is 40.42 in Guilan province. In component R, the most vulnerable were Talesh (99.66) and lowest for Lahijan (2.27), In component M, the highest vulnerability was for Rudbar (97.21) and the lowest for Talesh (24.30), In component A, the most vulnerable were Rasht (89.99) and the lowest for Anzali (2.21), In component C, the most vulnerable were Shaft (66.66) and lowest for Anzali (1.89), In component U, the most vulnerable were Rasht (67.55) and the lowest for Astara (28.92), In component E, the highest vulnerability was for Talesh (76.49) and lowest for Lahijan (22.69), In component G, the most vulnerable was reported to Rasht (53.05) and the lowest vulnerability was reported for Sunnelk (23.24).

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Assessing the Homogeneity of Temperature and Precipitation Data in Iran with Climatic Approach
 
Extended Abstract:
Qualitative evaluation and validation of atmospheric parameters such as precipitation and temperature are the most important condition for statistical analysis in climatic and hydrological researches. In addition, the meteorological and climatological data have a crucial role in transportation, agriculture, urbanization and health services.  Therefore, it is clear that using wrong data source for atmospheric investigations is the first hazard in natural hazards analysis. This study aimed to investigate the homogenization of minimum and maximum temperatures and precipitation data for 36 weather stations over different climatic classes in Iran. The Standard Normal Homogeneity Test (SNHT), (Alexanderson and Moberg, 1997), Pettit test (Pettit, 1979), Cumulative Deviation test (Buishand, 1982) and Worsley’s Likelihood Ratio test (Worsley, 1979) were carried out to study homogenization of minimum and maximum temperatures and precipitation data (1966-2015). The results revealed that 91.5 % and 88.5 % of minimum and maximum temperatures data, respectively, were in non-homogenized category. Although, Isfahan, Saghez and Gorgan for minimum temperature and Bandar-e Anzali, Sharekord, Kashan and Saghez for maximum temperature showed a homogenized condition with 5 % level of significance. The results showed most of the weather stations (28 out of 36 stations) had homogenized precipitation data. Even though, seven stations including Birjandd, Kerman, Kermanshah, Saghez, Sanandaj and Tabriz had homogenized precipitation data. The Urmia weather station was in doubtful class. That is precipitation data of Urmia weather station were homogenized by two tests results and were non-homogenized with other two tests of homogenization. The spatial distribution of trend variations of minimum temperature average was between -2.8 to 2.8 degree Celsius over the country. Moreover, maximum and minimum variations of minimum temperature occurred in northeast and northwest of the country, respectively. There were a significantly increasing trend (p<0.01) in most of the regions. The results also indicated that the significant variations happened for maximum temperature in most of the weather stations, mainly in northern half of the country. The minimum temperature jump was mostly found in 1985, 1994 and 1998 years during the study period (1966-2015). The maximum variations of minimum temperature were in Mashhad, Shahroud, Ahvaz, Yazd and Semnan weather stations with 2.8, 2.3, 2.2, 2 and 2 degrees Celsius, respectively, jump for above mentioned years during 1966-2015. In addition, the minimum change in minimum temperature was occurred in Birjand, Urmia and Bandar Abbas with a jump of 0.6 degrees Celsius. It should be mentioned that, unlike other stations, the Khorramabad (Lorestan Province) and Fasa (Fars Province) had a decreasing trend for minimum temperature. It changed from 10.3 to 8.3 and from 11.8 to 10.2 degrees Celsius in Khorramabad and Fasa, respectively. The results showed that the commencement of maximum temperature jump for most of the weather stations happened in 1998 with 1.1 degrees’ Celsius change. According to our study, a remarkable decrease in precipitation data was occurred in west and northwest of the country. There was a depletion of 80 to 150 millimeters from 1998 in Tabriz, Sanandaj, Saghez and Kermanshah weather stations during study period (1966-2015). Besides, 25 to 45 millimeters reduction in precipitation was found in south and southeast of the Country which has arid climate including Birjand (South Khorasan Province), Zabol (Sistan and Baluchestan Province) and Kerman. It was revealed that the variations of minimum temperature were larger than maximum temperature which was in agreement with results obtained by Rafati and Karimi, 2018. The results showed that the start of increasing maximum temperature in most of the weather stations was in 1998. It could be due to increasing the global temperature which is in accordance with results found by Steirou and Koutsoyiannis, 2012. The results revealed that about 80 % of precipitation data of weather stations were homogenized. These results were in agreement with results obtained by Hosseinzadeh Talaee et al., 2013. The results indicated that tests of homogenization for minimum and maximum temperatures and precipitation data could use in different climate over the country. Therefore, it could not allocate a single test to a particular climate type. In conclusion, it should be noted that before any analysis pertaining to environmental hazards, the calibration and maintenance of the weather instruments should be carried out periodically. In addition, the metadata and station history for relocation of the weather station should be checked. The relocation can create great changes in meteorological parameters due to elevation, latitude, longitude and land use/land cover differences between two sites.
 
Key Words: Homogeneity tests, Climate Data, Weather Station, Metadata
 
 
 

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Among the important challenges facing water resources of the country, one can mention the phenomenon of climate change and its impacts. The General Circulation Models (GCMs) can provide the best information about the response to increasing the concentration of greenhouse gases. Since the outputs of this model do not have sufficient time and space accuracy for studies on the effects of climate change, the output data of small general circulation models need to be quantitative. In this study, the SDSM statistical magnitudes and the CanemS2 model for climate change assessment, which are presented in the fifth report of the IPCC Comes under three scenarios RCP2.6, RCP4.5 and RCP8.5. The daily minimum temperature, maximum and precipitation rates of the synoptic station of Shahrekord (Cold mountain region) and Bandar Anzali (very humid and temperate climatic zone) are utilized and the parameters are for the period of 2040-2011, 2070-2070, and 2071-2099. Is. The results of the study show that the SDSM model has high accuracy and high efficiency in the climatic zone of very humid and temperate (Bandar Anzali) relative to the cold cliff (Shahrekord). However, the model has an acceptable ability to simulate the parameters in both areas. Under all three scenarios, RCP will experience the minimum and maximum temperature and precipitation in both climatic zones in all three times, but the cold climatic zone will be more affected by the climate change phenomenon.

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Evaluation the best of selective base period of GCM models to determine meteorological variables of Birjand station in future periods
 
Abstract:
Nowadays, determining the effect of a climate change in the various aspects of human life is quite evident. In such a situation, it is very important to determine the base period, which determines the effects of a climate change than in this period. Choosing a course-based course plays an important role in choosing future courses to conduct research on the effects of climate change. Many researchers in the research use the LARS-WG dynamic downscale method or the statistical method to measure the weather variables, which should be the same for the years of the base period and the upcoming period.
This research was conducted to select the appropriate base course for estimating minimum temperature, maximum temperature and precipitation at the synoptic station in Birjand. The station is located at latitude 32 degrees and 53 degrees east and 59 degrees and 17 degrees north latitude. In order to evaluate and accuracy of the methods in this research, seven criteria for estimating root mean square error (RMSE) and mean absolute error (MAE), relative error (RD), mean relative error of the month of the year (MRDM), average relative error of the month in the year (RDMM), PBIAS and RSR. In this study, using GCM models, we assessed the selected base courses for the synoptic station in Birjand. To doing in the research, an amount of 27 base courses from 35 models of the fifth report of the change were compared with similar periods obtained from the station in Birjand.
The results showed about precipitation that the duration of the base periods such as 1960-2005 and 1960-2000 is less of the RMSE and MAE errors than the rest of the courses, and the base period of 1965-1990 between periods less than 30 years and the period The 1990-1960s are also well suited to the precipitation data of the synoptic station. The maximum temperature of the 1960-1990, 1960-1985 and 1960-1995 is the lowest RMSE error. However, short-term courses of 1980-1960 and 1965-1985 present satisfactory results.In the case of minimum temperatures, periods of 21 and 31 years 1960-1980, 1960-1985, 1960-1990 and 1965- 1985 have a percentage error of RMSE and a lower percentage of PBIAS. Variable variation range can also be used to show the appropriate base course. The result showed that the periods 1960-2005 and 1970-2005 had a lower range of rainfall variation than the other variables and seems to be more suitable. However, courses such as 1990-2000, 1975-1995, and 1995-2005 have less certainty. The more courses that go into periods with shorter periods of time, the more modest and less certainty they will be. Also, if you look at changes in the 1975-2005 periods and the 1965-1995 periods, it will be clear how much each year towards the years closest to 2005 will be deducted from the precipitation daily average.
The results also show that maximum temperature changes are better than precipitation, and all courses have less variation range. Nevertheless, the period of 1960-2005 has the highest degree of certainty and the period of 1975-2005 has the least degree of certainty compared to the rest of the courses. In contrast to precipitation, there are periods such as 1970-1990, which, if considered as the basis for research, provide more certainty than the longer period of 1965-2005 for maximum temperature. Also, what's most clear about the maximum temperature is the higher the period with years closer to 2005, the temperature increases, which will increase the temperature over time.
The process of minimum temperature variations also indicates that in addition these changes are similar to the change in temperature, with the difference that the range of variations in the minimum temperature is somewhat higher than the maximum temperature. The period of 1960-2005 has the best degree of certainty and the period from 1975-2005 has the least degree of certainty than the rest of the courses. Although long periods of time are less certain than short periods, the result is that the longer the interval between periods increases, the more precise the results will be. The result is not entirely correct, 1975-2000 is less certainty than the 1965-2000 period and has better results in minimum temperatures. Therefore, the evaluation of selected periods of GCM models with similar periods from observations of Birjand station shows that for rainfall variables, periods with a number of years yield more satisfactory results, but for two variables the minimum temperature and maximum temperature of the periods, not long or short periods, provide less risk of RMSE and PBIAS than long periods.
Keywords: climate change, GCM model, base period, meteorological variable, emotion scenario
 
 
 

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Prediction of Climate Change in Western of Iran using Downscaling of HadCM3 Model under Different Scenarios
 
Hassan Zohrehvandi ¹, Ali Mohammad Khorshiddoust ², Behrouz Sari Sarraf ³

1- Ph.D student of Climatology, University of Tabriz, Email:  H.zohrehvandi@gmail.com 
Mobile number:+989181502513
2 - Associate Professor of Climatology, University of Tabriz, Email:         
 Mobile number:
 3- Associate Professor of Climatology, University of Tabriz, Email:      
 Mobile number:
 
Abstract
   Considering that water resources are at risk from climate change, the study of temperature and precipitation changes in the coming years can lead to droughts such as droughts, sudden floods, high evaporation and environmental degradation. To this end, global climate models (GCMs) are designed to assess climate change. The outputs of these models have low spatial accuracy. In order to increase the spatial accuracy of this data, downscaling methods are used which are divided into statistical and dynamic methods. One of the reasons for using these models is their quick and easy operation compared to other methods. Our study area consists of Kurdistan, Kermanshah and Hamedan provinces in the west of the country. In this study, observational data of minimum temperature, maximum temperature, precipitation and radiation of 6 synoptic stations in the studied area in the statistical period of 1961 until 2005. In this study, the LARS-WG model was used for downscaling of HadCM3 global model data. The LARS-WG model is one of the most popular weather generator models that which to generation for maximum and minimum temperature, rainfall and radiation are used daily under current and future climate conditions. This model as a downscaled version of the same process less complex and simulated data input and output, high ability to predict climate change. The HadCM3 model is also a type of atmospheric- oceanic circulation model developed at the Hadley Center for Climate Prediction and Research, which has a 2.5 degree latitude network at 3.75 degrees longitude. Also, three climate change scenarios A1B, A2 and B1 have been used, each of which reflects the characteristics of the world's economic growth, the world's population and social awareness. The methodology is that the model receives the monitored data of the basic course; by examining them the statistical characteristics of the data are extracted. Then, in order to validate and ensure the model's capability for the basic statistical period, the model is implemented to re-establish a series of artificial data in the base period. Then the outputs to evaluate the performance of the model in the reconstruction of the data, the statistical characteristics of observations to test and compare various criteria. MAE, MSE, RMSE and R2 criteria were used to evaluate and analyze the performance of the downscaling model. The results showed that the accuracy of the model varies in different stations and parameters, so that the model in simulation of temperature and radiation is more suitable than rainfall simulation. Also, the model has more successful in simulation of maximum temperature in comparison with minimum temperature. In sum, the results of different evaluation criteria indicate that the LARS-WG model has a good accuracy for the downscaling of the parameters studied in the study area. After evaluating the LARS-WG model and ensuring its appropriateness, the data was generated by the model for three climate change scenarios using the HadCM3 model. The results of the monthly review of the parameters studied at the station indicate that precipitation in the 2050s at all stations except Saralpul Zahab and Sanandaj stations according to the three scenarios studied in most months except December, January And at some stations, sometimes in November and February, they were lower than the base period, and rainfall is expected to decrease over the 20 years period (2046-2065), but the situation for Sanandaj and Saralpul Zahab stations is somewhat different, which, according to some scenarios, has increased in most months of the year, and according to some scenarios, rainfall has decreased in some months and it seems that the precipitation pattern is shifted The end of the warm season. But the rainfall situation is completely different in the 2080s, and rainfall has decreased in all stations and in most months of the year. The average monthly of the minimum and maximum temperatures as well as the amount of radiation shows that all three parameters will increase in all months of the year based on all three scenarios, as well as in the two decades studied (2080 and 2050) And its rate would increase in the decade than in the previous decade. According to the results, the amount of precipitation decreases in study area and the temperature and radiation will increase as well. The rate of precipitation decrease in the following periods will be 7.7% in the region than in the base period, and the minimum and maximum temperatures in the long-term was increase at the region 3.4 and 3.4 degrees Celsius, respectively, compared to the average period of the base. The radiation increase was 0.38 mJ /m² in Area level. The results of this research can help to solve the challenges of water resource managers and planners in future periods.
 
Keywords: Climate Change, downscaling, west of Iran, General Circulation model, LARS-WG
 
 
 
 

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The study and Comparison of desertification process on the basis of climate Criterion (Case Study: Abbas and Dehloran Plains, Ilam)
Soraya Yaghoobi, Kamran Karimi, Marzban Faramarzi
Abstract:
Nowdays desertification is a disaster in many countries , especially in developing countries. This problem includes natural factors and improper human activities. According to the expansion of desertification, providing the appropriate management methods will be reduced desertification intensity and its expansions. In this way, knowledge of processes of desertification and factors causing and  the intensifier it and also awareness of intensity and Weakness the processes and factors that are important and necessary   that should review and evaluate. Recognition criteria and indicators for provide a model to show the process of desertification and for determine one of the  best reason effective factors for prevent the spread of desertification factors is necessary. To knowledge and Trend of desertification and separation of  vulnerable  areas versus degradation factors .we should indentifi and evaluat  criteria and indicators affecte  at desertification. Therefore in this study of  the Iranian model IMDPA to assess trends and Comparison of desertification in recent years has used.
The studied area of  Dehloran plain is located in southeast of Ilam province (47^◦ 02′ 16″ to 47^◦ 25′ 07″ E and 32^◦ 48′ 33″ to 32^◦ 18′ 48″N) with an area of 54252  hectares, With precipitation  average 251.6 mm and Abbas plain is located in south of Ilam province(47^◦ 37′ 55″ to 47^◦  50′ 57″ E and  32^◦ 17′ 77″ to 32^◦ 29′ 25″N) with an area of 34104 hectares With precipitation  average 227.1mm. In this study, in dehloran plain of six stations in this Inside and outside the area also in Abbas plain of five  stations outside the area  used to measure the amount of rainfall in different seasons of year. In this study, to assessment  and Comparison of desertification in two study area of the Iranian model IMDPA used. In this study, of climate criteria, were used. which according to the IMDPA model for this criteria, indexes are considered for evaluation e.t.c: Climate criteria: (1) the amount of annual rainfall 2-drought indexe(SPI) 3. continuing drought In IMDPA model  All measurements  do in this work  units. To this end, first, working unit maps (geomorphologic facies) were created using slope, geology, and land use maps. a map was generated for each index according to assigned weights, such that the qualitative map of the desired criteria were obtained using the geometric mean of indicators.
The results earn  of  evaluation  of desertification  showed that  in the period  2005-2009  weight average of climate criteria is same with 1.50 all of the region are in the classe Middle sub class 1 and class low sub class3 . in the period  2010-2014  Also  weight average of climate criteria is same with 1.88 in classe Middle sub classes 2 and 3.  Also weight average of climate criteria in Abbas plain In the first period is same with 1.92  in the classe Middle sub classe2. Also In the second period with weight average is same 2.3 in classe Middle sub classes 2 and 3. The results showed that SPI index, as the most effective indexes, in plain Abbas In the first and second periods with the weighted average 3.04and 3.2 in the intense class under class 2 and 3. in front in Dehloran plain SPI index in the first and second periods with weighted average of 1.93 and 2.25 in the moderate classe and sub-classes 1, 2 and 3 and intense sub-classe 1.
In this study, to assess and Comparison of desertification Dehloran and Abbas Plains to provide regional model has done. . In this way  of  a criteria, also important and effective indexes belonging to this criteria of desertification used in dehloran and Abbas plains . The obtained results of the analysis criteria and Indexes indicated that in dehloran and Abbas plains in the first period ( 2005-2009) And second period (2010-2014)  between  indexes  the amount of annual rainfall, standard precipitation index (SPI) and drought duration Evaluated on the areas respectively standard precipitation index (SPI),  rainfall and drought duration index the most important factors in exacerbating desertification. Can be concluded that the intensity of desertification in Abbas plain compared to dehloran plain terms of climate is In more adverse conditions. In general, it can be concluded that desertification would intensify in future decades.
Keywords: Desertification, IMDPA, Climate, Abbas Plain, DehloranPlain
 

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The Spatio-Temporal Variations of Aerosol Concentration Using Remote Sensing in Sistan and Baluchestan Province (2018 - 2000)
 
 
 
Abstract
Atmospheric particles play an important role in balancing the energy budget of the Earth'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.
 
Keywords: Environmental Changes, Dust, Environmental Hazards, Climate.

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 Detection and Attribution of Changing in Seasonal variability cause of climate change (Case study: Hillsides of Central Southern Alborz Mountains)
Abstract
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.
 
Key words: Climate change, Detection, Attribution, Optimal Fingerprint, Hillsides of Central Southern Alborz Mountains