نشریه پژوهش های اقلیم شناسی
پیاپی 47 (پاییز 1400)

Rainfall is the most important source of water supply in the south and southwest of Iran. The Sudanese system is the most important cause of rain in these areas, which occurs mainly in the short and severe. Optimal survival is directly and indirectly related to the quantity and quality of these precipitations. As mentioned earlier, one of the most important characteristics of Sudanese systems in the south and southwest of Iran is their intensity. In the study of rainfall regime in the south and southwest of Iran, The lack of study of the intensity of rainfall in Sudanese systems, especially in recent decades, is quite evident. Therefore, in the present study, the trend of rainfall intensity of Sudanese systems from three independent input routes of Khuzestan, Bushehr and Hormozgan, which corresponds to the three main routes (A, B and C) of Lashkari (2002) research, is investigated. To use its findings in the management of water resources and atmospheric events such as floods and droughts.

In the present study, in order to analyze the trend of rainfall intensity of Sudanese systems in the triple routes of Khuzestan, Bushehr and Hormozgan, first 43 study stations were separated in the form of three routes. In the entrance route of Khuzestan included: 23 stations from the provinces of Khuzestan, Ilam, Kohgiluyeh and Boyer-Ahmad, Chaharmahal and Bakhtiari and Lorestan, in the entrance route of Bushehr included; 15 stations from Bushehr, Fars, Kohgiluyeh and Boyer-Ahmad provinces and at the entrance of Hormozgan, including; 7 stations of the province itself. Then, based on the rainfall incidence index of 1 mm and more in 50% of the stations of each route, the frequency of annual, monthly and continuous rainfall periods of Sudanese systems were identified using daily rainfall data (1995-2017) and then their average rainfall was extracted. Finally, after validating the data, the trend of rainfall intensity in Sudanese systems (2017-1995), using the methods; line of best fit, Man-Kendall and Sen’s slop estimator and based on the annual average, monthly and rainfall continuity periods, at 95% confidence level, it was checked in Visual Basic Excel environment and SPSS software

Sudanese systems operate in the cold period of the year in Iran (from the entrance routes of Khuzestan, Bushehr and Hormozgan). The rainfall of these systems in the triple routes is more 1 to 3 days, and rarely have rainfall of 5 days or more. The study of the trend of rainfall intensity of Sudanese systems (1995-2017) in the triple routes showed; the trend of rainfall intensity of these systems based on the annual average in the triple routes has a slope of decreasing changes of -0.04 to -0.1 mm during the period .However, these trends and the slope of changes are significant only in the rainfall of Bushehr route systems with a slope of very small decreasing changes (-0.1 mm during the period). The trend of rainfall intensity in Sudanese systems based on the monthly average of rainfall in the triple routes, is decreasing in some months and increasing in some. The slope of changes in these trends is in most cases zero and in some cases from 0.55 to -0.58 millimeters during the period, fluctuating. But in the meantime, only the increasing trend of November in Bushehr entrance route (0.55 one mm during the period) and the decreasing trend of February in Khuzestan entrance route (-0.21 mm during the period) is significant. The trend of rainfall intensity of Sudanese systems in the three routes is based on the average rainfall of different periods of continuity, in most cases decreasing and in some cases increasing. The slope of changes in these trends is in most cases zero and in some cases from 0.12 to -0.43 mm during the period. But in the meantime, only the decreasing trend of 2-day systems of Bushehr route with a slope of changes of -0.26 mm during the period is significant.

Despite the trend and slope of decreasing and increasing changes in the rainfall intensity of Sudanese systems, but due to the lack of significant trends and slope of changes (less than 0.6 ∓ one millimeter during the period), it can be concluded; The rainfall intensity of Sudanese systems in the entrance routes of Khuzestan, Bushehr and Hormozgan, lacks the trend and slope of significant changes at the 95% confidence level. But in general, Sudanese systems in most cases have short and heavy rainfall, and rarely light and long rainfall. As a result, the occurrence of heavy rains caused by Sudanese systems in the entrance routes of Khuzestan, Bushehr and Hormozgan should still be accepted as a principle. Therefore, floods are still one of the most important threats facing the study areas.

Droughts are one of the most spatially extensive disasters that are faced by societies. Despite the urgency to define mitigation strategies, little research has been done regarding droughts related to climate change. The challenges are due to the complexity of droughts and to future precipitation uncertainty from Global Climate Models (GCMs). It is well-known that climate change will have more impact on developing countries. Among the most significant impacts of droughts to the environment are the acceleration of desertification processes, the increase in the risk of forest fires, the reduction of the availability of water resources for domestic and industrial use and the damage done to animals and vegetation These facts made the complexity of this phenomenon explicit. For instance, droughts are initiated by a meteorological drought, then they generate a hydrological drought, which may produce an agricultural drought and, in cases of prolonged occurrence, may cause a socio-economic drought. The final stage of a socio-economic drought may cause negative impacts, such as the loss of crops and livestock, a decrease in hydroelectric generation, migration, landscape degradation or social conflicts, among others. The main aims of this study were to determine drought occurrence periods and intensities in southern Iran by different drought indices (1), to compare different drought indices (2), Estimating the probability of drought occurring in the future for southern Iran.

Study area The coastal city of Bandar Abbas is the capital of Hormozgan province and is located in the south of Iran. This city is located in the form of a coastal strip in the north of the Strait of Hormuz. The coordinates of the area include 27°11' to 27° 12' 30" North 56° 20' to 56° 21' East with an area of 0.913 square kilometers. The average annual rainfall during a 57-year statistical period (1957 to 2010) in Bandar Abbas is 172.6 mm. During the wet season (November to April) the rainfall is 94% of the annual rainfall and during it. In the dry season, the rainfall is 6% of the annual rainfall.

In the research In order to monitor and evaluate drought assess the representation of droughts from statically down scaled GCMs in the present and evaluate the temporal structure and variability of future meteorological droughts in the south of Iran under RCP 4.5 and RCP 8.5 scenarios. This is done by using products (MPI-ESM-MR) from the Coupled Model Intercomparison Project 5 (CMIP5) of the Third National Communication on Climate Change. The Standardized Precipitation Index (SPI) and Markov chain for droughts Possibilities were used to characterize extreme, severe and moderate droughts in the present (period 1982–2005) and the future (period 2016–2045). This study contributes to the spatial and temporal characterization of present and future droughts, and offers a contrasting analysis between them. In order to evaluate the efficiency of the down scaled method have been used the mean relative error (MRE), root-mean-square error of RMSE and MAE.

The results of downscale methods showed that the CF-variance method has better correlation and less error than the observed data. The results of the station Markov chain showed that the highest probability is related to dry to normal in summer and dry to wet or normal with 77.8% and 42.9%, respectively. According to the SPI index, the study area will experience more severe and prolonged droughts in the future according to both scenarios of atmospheric circulation model than the historical period. According to Scenario 4.5, with increasing the timescale of SPI, the severity of drought has decreased, so that according to the 6-month SPI, the drought has an intensity of -1.83 and according to the annual SPI has an intensity of -1.66. In the 6-month period, the average dry class and in the 9- and 12-month periods, the normal to wet class have the highest frequency. According to scenario 8.5 according to the SPI classification, autumn and summer are in the near normal (mild) class. Winter and spring fluctuate between drought and non-drought. In Part B, the index ranges from 6, 9 and 12 months in the normal to non-drought grade. Markov's probability should increase from dry to wet for months with one class. In other months, such as April, May, June and July, we probably see different things. These results are similar to the 4.5 scenario, which shows more probabilities in the normal class for several months on average. Comparison of the results of Markov chain probability for the base and future period showed that the probability of wet to dry class occurring for spring, summer and winter seasons is so that the probability of this class occurring for the base and future period according to scenario 4.5 and 8.5 It is equal to 57, 60 and 60 percent for spring, respectively. It is equal to 77.8, 67 and 50 percent for summer and 66.7, 75 and 75 percent for winter, respectively. The results showed that the probability of Markov chain for autumn in the base period from wet to normal with 89% probability to dry class to normal for the next period according to scenario 4.5 and 8.5 is equal to 89 and 90%, respectively. The results from the time periods of 6, 9 and 12 months showed that the probability of occurrence of Markov chain classes for similar scenarios of 4.5 and 8.5 are slightly different with a small percentage of probability. In examining the possibility of drought with the Markov chain, it was observed that each floor tends to move to its nearest floor. A similar issue has been reported in studies (Moreira et al., 2006; Paulo and Pereira, 2007; Yeh et al., 2014) in the study of drought using the Markov chain. According to the diagrams presented for both scenarios, the most probable is related to the normal class. According to the results, with the increase of wet season and drought, the possibility of stagnation has decreased.

The Intergovernmental Panel on Climate Change (IPCC) fifth assessment report has pointed out that global warming is intensifying, and the frequency and intensity of extreme climate events such as high temperature and heavy rainfall will increase significantly (IPCC 2013). The weather and climate disasters have also increased and had a very serious impact on social stability, economic development and people’s lives . Probabilistic analysis of drought events plays an important role for an appropriate planning and management of water resources systems, especially in arid or semi-arid regions characterized by low annual or seasonal precipitation. In particular, estimation of drought return periods can provide useful information for a proper water use under drought conditions. As the changes in the frequency and intensity of extreme drought events bring great threats to natural and social systems, studies on the drought events, especially on the analysis of statistical characteristics of extreme drought events, have attracted the attention of an increasing number of scholars in the recent decades.This study aimed to project future SPEI using RCP8.5 and RCP4.5 projection data. The drought characteristics by the threshold level to the projected SPEI were identified. This study also projected the drought risk of each station (representing four climate zones of Iran) in the 21st century by fitting the drought characteristics to the Generalized Extreme Value (GEV) distribution.In this study, the analysis of extreme values of standardized precipitation-evapotranspiration index (SPEI) was performed to evaluate the potential future changes in drought characteristics in Keramn, Sharekord, Ahvaz and Bandar Anzali stations which are representing the warm and arid, cold and semi-arid, warm and semi-arid and humid climate respectively. The capability of three global climate models in simulating temperature and precipitation during the base period (1976-2005) for four climate zones of Iran (Ahvaz, Bandar Anzali, Shahrekord and Kerman) was investigated. Drought extreme events (SPEI≤-1) were identified from the monthly series for the base period and the period 2021-2051 under the RCP scenarios, and the characteristics of intensity, severity and duration were extracted.

As it is difficult to define when the drought started and ended, previous researches assessed the risk of drought in an indirect way by conducting the frequency analysis of the drought indexes.SPEI can reflect the effect of not only the variability in precipitation but also the variability of evapotranspiration. Thus, this study used SPEI. SPEI is the difference between the random month 𝑖 and PET obtained by using the precipitation and the Hargreaves & Samani (1982) equation, as shown in D= P_i- 〖PET〗_i (1) Which is synthesized in each time scale like D_n^k=∑_(i=0)^(k-1)▒〖P_(n-i)-〖PET〗_(n-i) 〗 (2) Here, 𝑘 is the time scale of synthesis, and 𝑛 is the month used for calculation. The severity and duration and drought intensity were calculated using SPEI. Negative SPEIs mean the dry condition; a drought event is defined when the SPEI is continuously negative and reaches a value of “−1.0” or less. Thus, it is assumed that “−1.0” is the threshold level and that the drought starts in the level lower than “−1.0” in monthly SPEI. The aggregate of SPEI while one drought event lasts was defined as the severity of drought.

The results showed that GCMs in the stations under study (except Bandar Anzali), have good skill in simulating the variables of temperature, precipitation, frequency and drought classes. In Ahvaz under RCP4.5 scenario, the frequency of moderate events and under RCP8.5 scenario, the frequency of severe class will likely be more than the base period. For the same return periods, the extreme drought intensity values under RCP8.5 scenario with the base period will be significantly different. Under the opposite scenario, the intensity decreases slightly compared to the baseline period. For Shahrekord, the frequency of the middle class of this phenomenon under two scenarios projected to decrease compared to the 1975-2005 priod and in contrast to the frequency of severe types projected to increase. Changes in Probability Density Function (PDF) of GEV for Shahrekord station showed that the mean and variability of the intensity of extreme values of this phenomenon will likely increase under two scenarios. At Kerman station, the frequency of severe drought under RCP4.5 scenario increase compared to the base period. Under the RCP8.5 scenario, droughts projected to be reduced.

The phenomenon of dust as one of the atmospheric hazards in many countries in arid and semi-arid regions of the world can have devastating effects on various aspects of human life, including health, ecology, transportation, industry, energy, etc. Iran is located in a part of the dust belt that stretches from the Sahara Desert to West and Central Asia, and every year dust storms occur in different parts of the country and cause a lot of damage. Recent droughts have also intensified internal dust sources in the Middle East and Iran. In this study, in order to investigate the temporal and spatial pattern of dust phenomenon in the Middle East, Dust Storm Index (DSI) and horizontal visibility in West Asia have been analyzed. In order to calculate the dust storm index and to study the horizontal visibility, the observed data of synoptic stations in the study area in the period 2009 to 2018 were used, which include Iran, Iraq, Syria, Turkey, Tajikistan, Uzbekistan, Turkmenistan, Armenia, Azerbaijan, Georgia, parts of Russia, Kazakhstan, Afghanistan, Pakistan, Yemen, Oman, UAE, Qatar, Bahrain, Saudi Arabia and Jordan. The monthly and annual DSI averages showed that this index is suitable for monitoring wind erosion on a large scale using meteorological records that calculate the frequency and intensity of dust storms in the region. This index is a combination of three indices of stormy days with local dust, moderate dust storm and severe dust storm, which are calculated based on the number of times the dust phenomenon codes reported in synoptic stations. The results showed that the changes of DSI in the whole study area have increased from 2009 to 2015; but from 2015 to 2018, this trend has been decreased. The DSI index provides a comprehensive index of the trend of change in the region, taking into account both local and non-local dust. Studies show that the DSI index in most countries in the study area is highly dependent on local dust storms and moderate or non-local dust storms have less impact on these countries. The study of the frequency of dust events shows that during this study period, based on the report of the frequency of the dust phenomenon of non-local origin and visibility of less than 5000 meters in spring, it was determined that the western and southwestern regions of the country are more affected by the phenomenon of non-local dust sources. The similar pattern prevails in spring for the number of dusty days with a decrease in visibility in the north, south and southeast, east and northeast and the center of the country. Meanwhile, the number of dusty days in the western and southwestern part of the country is higher in summer than in spring. In autumn, the frequency of dusty days with a less than 5,000 meters visibility reduction in all regions of the country has sharply decreased compared to spring and summer. The trend in the frequency of dust-related phenomena in winter follows the autumn regime and approximately with the same intensity in the areas of east and northeast, south and southeast, northwest and center. The study of the average monthly, seasonal and annual horizontal visibility showed that in the central and eastern regions of Saudi Arabia and southwestern Iran, more than 60 days with dust has led to a decrease in visibility to less than 800 meters. In addition, in the Makran coast in southern Pakistan and in the Sistan region, the highest number of dusty days with a decrease in visibility between 800 and 1500 meters has been reported. It was also observed that in summer in the Sistan region more than 100 days of dust with a decrease in visibility of less than 800 meters has been recorded. Also, in this season, in addition to the Sistan region, dust events on the coasts of Makran, southwestern Iran and eastern Saudi Arabia have reduced the horizontal visibility between 800 to 1500 meters. In general, the largest decrease in visibility in May is in the eastern part of Saudi Arabia near the shores of the Persian Gulf, in the southern part of Kuwait, as well as in the northern part of Saudi Arabia on the Iraqi border and in the central part of Iraq. In June and July, in the eastern parts of Iraq, western and southwestern Iran, as well as in part of northern Pakistan on the Afghanistan border, between 10 and 20 days of dust in the horizontal range of less than 800 meters were reported. Moreover, the annual study of the decrease in horizontal visibility due to dust events showed that the largest decrease in visibility was recorded in 2010, so that in the northern parts of Saudi Arabia on the Iraqi border and small parts of central and eastern Iraq on the border between Iran and southwestern Iran between 10-20 days and in the southwest of Pakistan on the coast of Makran between 50-60 days of dust events in the horizontal visibility range of 800 to 1500 meters has been reported.

It is necessary to understand the climate change in next decades to have a suitable environmental planning for adapting and reducing it's effects. In climate change studies, ignoring uncertainties at various stages of impact assessment will reduce confidence in system results. This uncertainty is due to the performance of general circulation models, different emission scenarios and doanscaling process. In this research uncertainty of monthly average temperature of drainage basin of Minab dam is projected in two periods(2016-2045 and 2046-2075) using outcomes of the five general circulation models of the HADGEM2-ES, BNU-ESM, CCSM4, CSIRO-MK3-6, MPI-ESM-MR under three scenarios of RCP2.6, RCP4.5 and RCP8.5. For this purpose, using the method of variants of average tempreture for change factor, future is downscaled. Latin hypercube method wich is an accidental sampelling is used here for checking the uncertainty of models. The results of period uncertainty, in all models and scenarios showed that, the uncertainty of the second period (2046-2075) is greater than in the first period (2016-2045). It means, increasing the length of the forecast period increases the error in predicting climate change models. The results showed the uncertainty of the different models showed that the least uncertainty was related to the CSIRO-Mk3-6 model in the RCP2.6 scenario and the 2016-2045 period, while the highest uncertainty was related to the HadGEM2-ES model in the scenario and period.It is necessary to understand the climate change in next decades to have a suitable environmental planning for adapting and reducing it's effects. In climate change studies, ignoring uncertainties at various stages of impact assessment will reduce confidence in system results. This uncertainty is due to the performance of general circulation models, different emission scenarios and doanscaling process. In this research uncertainty of monthly average temperature of drainage basin of Minab dam is projected in two periods(2016-2045 and 2046-2075) using outcomes of the five general circulation models of the HADGEM2-ES, BNU-ESM, CCSM4, CSIRO-MK3-6, MPI-ESM-MR under three scenarios of RCP2.6, RCP4.5 and RCP8.5. For this purpose, using the method of variants of average tempreture for change factor, future is downscaled. Latin hypercube method wich is an accidental sampelling is used here for checking the uncertainty of models. The results of period uncertainty, in all models and scenarios showed that, the uncertainty of the second period (2046-2075) is greater than in the first period (2016-2045). It means, increasing the length of the forecast period increases the error in predicting climate change models. The results showed the uncertainty of the different models showed that the least uncertainty was related to the CSIRO-Mk3-6 model in the RCP2.6 scenario and the 2016-2045 period, while the highest uncertainty was related to the HadGEM2-ES model in the scenario and period.It is necessary to understand the climate change in next decades to have a suitable environmental planning for adapting and reducing it's effects. In climate change studies, ignoring uncertainties at various stages of impact assessment will reduce confidence in system results. This uncertainty is due to the performance of general circulation models, different emission scenarios and doanscaling process. In this research uncertainty of monthly average temperature of drainage basin of Minab dam is projected in two periods(2016-2045 and 2046-2075) using outcomes of the five general circulation models of the HADGEM2-ES, BNU-ESM, CCSM4, CSIRO-MK3-6, MPI-ESM-MR under three scenarios of RCP2.6, RCP4.5 and RCP8.5. For this purpose, using the method of variants of average tempreture for change factor, future is downscaled. Latin hypercube method wich is an accidental sampelling is used here for checking the uncertainty of models. The results of period uncertainty, in all models and scenarios showed that, the uncertainty of the second period (2046-2075) is greater than in the first period (2016-2045). It means, increasing the length of the forecast period increases the error in predicting climate change models. The results showed the uncertainty of the different models showed that the least uncertainty was related to the CSIRO-Mk3-6 model in the RCP2.6 scenario and the 2016-2045 period, while the highest uncertainty was related to the HadGEM2-ES model in the scenario and period.It is necessary to understand the climate change in next decades to have a suitable environmental planning for adapting and reducing it's effects. In climate change studies, ignoring uncertainties at various stages of impact assessment will reduce confidence in system results. This uncertainty is due to the performance of general circulation models, different emission scenarios and doanscaling process. In this research uncertainty of monthly average temperature of drainage basin of Minab dam is projected in two periods(2016-2045 and 2046-2075) using outcomes of the five general circulation models of the HADGEM2-ES, BNU-ESM, CCSM4, CSIRO-MK3-6, MPI-ESM-MR under three scenarios of RCP2.6, RCP4.5 and RCP8.5. For this purpose, using the method of variants of average tempreture for change factor, future is downscaled. Latin hypercube method wich is an accidental sampelling is used here for checking the uncertainty of models. The results of period uncertainty, in all models and scenarios showed that, the uncertainty of the second period (2046-2075) is greater than in the first period (2016-2045). It means, increasing the length of the forecast period increases the error in predicting climate change models. The results showed the uncertainty of the different models showed that the least uncertainty was related to the CSIRO-Mk3-6 model in the RCP2.6 scenario and the 2016-2045 period, while the highest uncertainty was related to the HadGEM2-ES model in the scenario and period.

One of the greatest human problems in the present and future is the scarcity of water resources and the inadequate distribution of water resources. One of the important climatic elements and parameters in each region is the prediction and estimation of precipitation and temperature, and this helps planners to achieve good harvest patterns from water sources and to farmers for crop management, especially dry farming. In addition, it reduces the likelihood of irreparable damage caused by droughts and floods. Precipitation is an important source of water throughout the world, including arid and semi-arid regions. The element of precipitation is one of the most complex natural phenomena. In fact, the tangible and intangible effects are so scary that they move from an explicit legal system to a complex structure. Another important factor affecting water resources is the temperature element. Therefore, investigation and prediction of temperature in each region and catchment can be of great importance for optimal utilization of water resources, reduction of evapotranspiration, etc.The purpose of this study is to predict the precipitation and temperature in Sistan and Baluchestan province. Due to climate change and the downward trend of precipitation and increase in temperature in the region, it is attempted to predict precipitation and temperature in 13 Next year, better management of the use of water resources in the agricultural and non-agricultural sectors.

General pattern of non-seasonal and seasonal order A) Degree seasonal autoregressive process The general model of non-seasonal and seasonal order is shown in relation (1) The seasonal autoregressive pattern can be regarded as a special case of the ordinary non-seasonal autoregressive model in which the coefficients are only zero at the seasonal delays . (B) The process of the seasonally moving average of the degree Q of this process is expressed in (2):

In the present study, the Akaike criterion based on the residuals of the fitted model and the best fitted model criterion were selected from all the models for the Hirmand catchment. According to the criteria, the best model is the one that has the least value P- Value, and the highest T. The results of these criteria were identified. The SARIMA model has the least AIC and SBC value. Results of different SARIMA models of time series and criteria,P- Value, AIC ,SBC and T. Table 2 shows the catchment rainfall. For the catchment only in the model , the absolute value T of all parameters except the constant is greater than 2 and it is less than 0.05 and also has the lowest criteria AIC and SBC values, so the best model is known.Table (4) shows the comparison of different time series models for the static temperature data of the Hirmand catchment over the 18-year period (2001-2019). According to the criteria P-Value, ، ، and . the best model is the one that has the least value, and the highest. The results of these criteria were identified. The model has the least value and. Results of different models of time series and criteria ، ، and . Table (4) shows the catchment temperature. For the catchment temperature only in the model the absolute value of all parameters except the constant is more than 2 and it is less than 0.05 and also has the lowest values of criteria and, therefore, the best model is known.

In this study, the results of Lajang-Box test for the selected model of monthly temperature and precipitation of the Hirmand catchment for all delays were P-VALUE greater than 5%, which confirms the hypothesis that the residuals are uncorrelated. Also with varying degrees of freedom the test statistic is less than 5% of the kiddo table and the null hypothesis is accepted. The monthly temperature of the Hirmand basin has an increasing trend and the monthly rainfall has a decreasing trend, so planners and policymakers should apply policies to optimize water use in the region. 1- Optimal use of water resources is recommended in case of increase of temperature and decrease of bonding in the area. 2- Manage irrigation water during drought and allocate water to more essential crops and crops that have a comparative advantage. Applying the knowledge of experts in agriculture, plant breeding and climatology to adapt the type and extent of crop cultivation to climatic conditions.

In recent years, fires in vegetation areas have increased and have left a lot of destructive effects. Due to the fact that forest fires are affected by various factors. Therefore, this article examines the methods of monitoring and predicting fires in vegetative areas of Iran and the world. A review of research conducted in Iran shows that studies related to fire in our country have used the method of hierarchical analysis to weigh the effective factors and GIS in the occurrence of forest fires. The selection of effective variables in fire as well as fire risk modeling has been used and in more advanced methods the combination of fuzzy inference system and neural network, artificial intelligence and support vector machine has been used to predict future fires. Satellite images and remote sensing have been discussed. It should be noted that the use of fire product images in Iran is one of the nascent researches. Its advantage over other methods is that while they are cheap, they are also very fast, and the results are more up-to-date.The results of studies conducted in other countries show that most types of vegetation, slope, geographical direction, distance from roads, topography and land use have been the most effective factors in monitoring forest fires and the integration of layers is usually based on hierarchy and risk factor in The occurrence of fires has been done. Also, indicators that are effective from environmental and climatic factors have been used in fire monitoring and forecasting, from the notable activities of other countries, preparation of fire monitoring and forecasting map It is active for timely detection of fires with satellite images and spectral indicators, which has resulted in favorable results for optimal decisions to extinguish and prevent fires. Researchers in recent research have examined the evaluation of monitoring indicators. They set fire to the U.S. Fire Risk Rating System as a comprehensive and desirable indicator compared to other similar methods in monitoring forest and rangeland fires.Summary of research on fire potential detection in different parts of the world shows that GIS is very effective for developing information, management and forecasting of forest fire activities, so that creating a database in GIS consisting of variables affecting the occurrence of fire risk areas. The occurrence of forest fires is very useful. In most cases, vegetation type, slope, geographical direction, distance from permanent roads and rivers, topography and land use are the most effective factors in the occurrence of fire and the integration of layers is usually based on the hierarchy and risk factors in the occurrence of fire. In other studies, the fuzzy hierarchical analysis method, in other words, the combination of hierarchical analysis and fuzzy sets has been used to model the risk of fire. Satellite TM images have also been used to identify past fires. The general results of these studies show that in hot climates with dry vegetation, high slope, south direction and close to roads and residential areas, the potential for fire risk is high. To evaluate the accuracy of the method and model used in preparing the fire potential map, the map of critical fire areas is usually compared with the map of areas that have caught fire in the past, and if the two match, the model used seems desirable. In some other studies, a combination of fuzzy analysis system and neural network, artificial intelligence and support vector machine has been used to predict fire behavior management. Also, in more advanced methods, logistic regression and decision tree algorithm have been used to select and map effective variables in fire as well as fire risk modeling. Other studies have predicted the risk of fire and prepared a fire risk map by satellite imagery and helped to improve and correct the correct detection of satellite imagery by changing existing algorithms. And in their latest study, the combination of fire weather indicators with satellite imagery helped to detect the occurrence of fire in a timely and accurate manner.Studies related to the potential of fire in Iran have been mostly in the field of zoning of fire risk from various factors and GIS as well as hierarchical analysis. To predict the occurrence of forest fires, artificial neural networks and climate data have been used. Predictions of fires in forests and pastures have also been made. And in the latest studies, they examined satellite fire products for accurate and timely forecasting. The results show that Madis sensor images have potential and good potential in fire detection and monitoring, and due to the newness of research in this field, there is a lot of activity. Each of the methods of modeling and fire risk assessment that were expressed in this study, advantages And have their own disadvantages; The use of indigenous models for fire hazard mapping definitely results in better results than non-indigenous models, so to use any method to improve the validity of models and methods of fire prediction and monitoring, all environmental and climatic factors suitable for the indigenous Consider instrumentation.

IntroductionTeleconnection patterns indicates a large variations that happen in the patterns of atmospheric waves and jet streams. Teleconnections are associated with the anomaly of atmospheric large scale and hemisphere circulation. Teleconnection patterns affecting on the amounts of temperature, precipitation, location and intensity of jet streams in the extensive regions. Arctic Oscillation is the leading mode of extratropical circulation from the surface to the lower-level stratosphere in the northern hemisphere(Hojati & Masoodian, 2018). Arctic Oscillation as one of the teleconnection patterns of the north hemisphere includes an atmospheric pressure anomaly in sea level in the north polar latitudes and middle latitude (45oN); that is shown in negative and positive phases(khosravi et al, 2007). Fluctuations in the Arctic Oscillation create a seesaw pattern in which atmospheric pressure at polar and middle latitudes fluctuates between negative and positive phases(Hojati& Masoodian, 2018). The AO pattern controls pressure, temperature and precipitation changes patterns and geopotential level, wind azimuth, in specific periods of year. Arctic Oscillation(AO) is one of the effective patterns in general circulation of atmosphere variability in extra-tropic region of northern hemisphere and is the main factors in controlling climate elements such as temperature and precipitation. Meanwhile temperature is one of climatic impressible element of the other climatic affective elements which is under the effect of this pattern (khosravi et al, 2007). Since temperature is a main element in the formation of the climate, and its variations can alter the climate structure of each region, therefore, studying the relationship of teleconnection patterns and temperature variability has a large part to be climatological research. In this regard, numerous researches has been carried out on the relationship of Arctic Oscillation (AO) and temperature changes, and the identification and analysis of the patterns of temperature has been considered by different methods in numerous studies. Here, we can be mention researchers such as Thompson and Wallace(1998), Wallace(2000), Thompson(2000), Cintia(2000), Roger et al(2000), Gong et al(2002), Philip et al(2002), Gong and Wang(2003), Daoyi and Shaou(2003), Wang et al(2005), Bond and Harrison(2006), Turkes and Erlat(2008), Sun and Ahn(2014) and in Iran, Masoodian(2005), Nazemalsadat et al(2006), Khosaravi et al(2007), Nasr isfahani et al(2008), Khoshakhlagh et al(2008), Esfandiari et al(2009), Khorshiddost et al(2010) and Alizadeh et al(2011). Since the identification of temperature patterns and its variations is very important for management and planning, this research seeks to evaluate and analyze the relationship of Arctic Oscillation (AO) and winter temperature variability on the north- west Iran.Materials and methodsIn this research we examined the effect of this oscillation positive and negative phases on north-west winter temperature of country during 1965-2010. To achieve this goals, two databases was used. First, the mean temperature data of north-west synoptic stations of country and second, atmospheric data including sea level peressure variable ( hPa) and geopotential height(m). Atmospheric data were recieved from NCEP/NCAR. To study these data, a framework has been considered between -40-120 degree eastern and 0 - 80 degree northern with resolution of 2.5*2.5 degree that have been borrowed from database of (NCEP/NCAR) dependent to National Atmosphere and Oceanography Institute of USA. Then, agglomerative hierarchical cluster analysis on the basis of ward linkage method have been done on data of negative and positive phases in Matlab software. A three groups classification has been obtained for negative phase and the other 3 groups classification for positive phase. Each group was assigned to an exponent month. After analyzing the patterns, we educed the patterns related to these phases. Finally, we studied the relationship between Arctic Oscillation exponent and north- west winter temperature by using correlation method. All of these steps and calculations conducted by using Matlab, GrADS and Surfer. Results and discussionObtained results represented that the temperature value in negative phase groups was lower than positive phases. Whereas this temperature condition was increased during positive phases. According to pressure patterns deployment during negative phase and monthly temperature average, we can deduce that in this phase, the cooler and more stable conditions have prevailed in studied region than positive phase and consequently, the higher temperature condition has been accurred in positive phase than negative phase. ConclusionStudying the relationship of the exponent with winter temperature in north-west indicate the inverse and negative correlation coeficient with Arctic Oscillation.

In recent years, the dust storm has been caused extensive economic, health and environmental damages. Dust storms are lower atmosphere events that result from wind erosion liberating sediment participles from the ground surface. Sand storms occur relatively close to the ground surface, but finer dust particles may be lifted kilometers high into the atmosphere, where strong winds transport them long distances. Increasing Dust storm events have several negative effects on the environment. Airborne dust causes or aggravates human health problems. Chronic exposure to fine particulates is associated with premature death due to cardiovascular and respiratory disease, lung cancer, and acute lower respiratory infections. Inhalation of fine dust particles exposes individuals not only to hazardous fine mineral particulates, but also to harmful combinations of pollutants, spores, bacteria, fungi, and potential allergens carried along with mineral dust. Poor visibility, sand movement and deposition as a result of SDS also increase incidences of road accidents and aviation hazards.For these reasons, enough knowledge about the origin and occurrence time of dust storms could be effective in its damage reduction. The main objective of this research was the source identification of Karaj dust storm events directionally. For this purpose, Dust events in about two decades (2000-2018) were extracted from meteorological data of Karaj synoptic station. PM10 data from Karaj air quality monitoring stations and MODIS Aerosol Product were used to verify dust events that were determined in the previous step based on availability. The MODIS Aerosol Product monitors the ambient aerosol optical thickness over the oceans globally and over the continents. Furthermore, the aerosol size distribution is derived over the oceans, and the aerosol type is derived over the continents. CPF and DRS diagrams and HYSPLIT model were applied for source identification. The Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a computer model that is used to compute air parcel trajectories to determine how far and in what direction a parcel of air, and subsequently air pollutants, will travel. HYSPLIT is also capable of calculating air pollutant dispersion, chemical transformation, and deposition. The model derives its name from the usage of both Lagrangian and Eulerian approaches. The Conditional Probability Function (CPF) calculates the probability that a source is located within a particular wind direction sector CPF is useful in determining the direction of a source from a receptor site. The DRS approach, the ratio of the concentration contribution weighted-wind sector abundance to the overall wind abundance in a wind sector is used to assess the pollution potential of the source in that sector.Based on meteorological data and considering AOD images, the maximum and minimum numbers of the dust storm in the year had been recorded in 2008 and 2000, respectively. Also, the most dust storm occurred in summer. The most frequent dust storms were observed from April to August. Among these months, June has the most frequency of dust storms. The dust storm events increase from the beginning of the warm-season and reduce in fall when the wet and cold season starts.Results of windrose, CPF, windrose of dust events and DRS showed the important direction in dust events of Karaj were NWW, NW. HYSPLIT results for 72 h back trajectory and dust events of Karaj in three altitudes above ground level (10, 500 and 1000 m) showed the dust origin in NW and W of Karaj had the most share on Karaj dust storm events. For example, two dust events results were presented. One of them belongs to local dust events and another for regional dust events. The results helped to identify the most important dust event origins. In overall, the results of directional source identification of Karaj dust storm showed the sources that are located in W of Karaj (in local events) and IRAQ and the north of Saudi Arabia (in regional events) have important role to increase particulate matters (PM) and decrease air quality of Karaj. In addition the results of investigation of MODIS Images for studied dust events that were the real snapshot of the earth showed the results of HYSPLIT is reliable to determine the dust storm sources. For example in 2014/4/13, MODIS image showed the source of this event was from north of Saudi Arabia and the passed above SW border of IRAN and the arrived to Karaj. This event have long journey more than 1000 km and showed the importance of dust origin that are located in other countries to air quality of Karaj. In conclusion, directional source identification of dust storm was successfully and very rapid method to understand the most important sources in any events. This methodology can be used in other studies that have concern about source identification for dust storm.

Various factors play a role in climate change, including oceanic processes (such as ocean orbits), changes in solar radiation received from the earth, volcanic eruptions, and changes in human activity. Climate change has affected atmospheric and oceanic phenomena such as the Earth's surface temperature, precipitation, sea level, wind speed, wave height, coastlines and other phenomena and has changed these characteristics. In studying the effects of climate change on oceanic phenomena, estimating the long-term impact of climate change is of great importance. This is important because the offshore structures in these areas are affected by these changes and there is a need to amend the standard guidelines for the design of coastal structures. In this study, using the CGCM3 climate change model, wind speed values ​​for the Persian Gulf and the Sea of ​​Oman were extracted. These values ​​as model input, using the third generation numerical model MIKE21 (SW) which simulates wave parameters with the least assumptions, were converted into wave characteristics and finally the amount of changes in wave characteristics such as wave height Wave period and wave direction have been obtained in the Persian Gulf, the Sea of ​​Oman and part of the Indian Ocean that is affected by climate change in the region. The results of studies have shown that the effect of climate change on wave height and wave period is negligible, but in Makran beaches the effect of climate change on the angle of the wave direction can be considered and this will affect the sedimentation process. The effect of climate change on the wave direction and its effect on the structure should be further investigated for the construction of ports and marine structures such as desalination catchments in these areas.Various factors play a role in climate change, including oceanic processes (such as ocean orbits), changes in solar radiation received from the earth, volcanic eruptions, and changes in human activity. Climate change has affected atmospheric and oceanic phenomena such as the Earth's surface temperature, precipitation, sea level, wind speed, wave height, coastlines and other phenomena and has changed these characteristics. In studying the effects of climate change on oceanic phenomena, estimating the long-term impact of climate change is of great importance. This is important because the offshore structures in these areas are affected by these changes and there is a need to amend the standard guidelines for the design of coastal structures. In this study, using the CGCM3 climate change model, wind speed values ​​for the Persian Gulf and the Sea of ​​Oman were extracted. These values ​​as model input, using the third generation numerical model MIKE21 (SW) which simulates wave parameters with the least assumptions, were converted into wave characteristics and finally the amount of changes in wave characteristics such as wave height Wave period and wave direction have been obtained in the Persian Gulf, the Sea of ​​Oman and part of the Indian Ocean that is affected by climate change in the region. The results of studies have shown that the effect of climate change on wave height and wave period is negligible, but in Makran beaches the effect of climate change on the angle of the wave direction can be considered and this will affect the sedimentation process. The effect of climate change on the wave direction and its effect on the structure should be further investigated for the construction of ports and marine structures such as desalination catchments in these areas.Various factors play a role in climate change, including oceanic processes (such as ocean orbits), changes in solar radiation received from the earth, volcanic eruptions, and changes in human activity. Climate change has affected atmospheric and oceanic phenomena such as the Earth's surface temperature, precipitation, sea level, wind speed, wave height, coastlines and other phenomena and has changed these characteristics. In studying the effects of climate change on oceanic phenomena, estimating the long-term impact of climate change is of great importance. This is important because the offshore structures in these areas are affected by these changes and there is a need to amend the standard guidelines for the design of coastal structures. In this study, using the CGCM3 climate change model, wind speed values ​​for the Persian Gulf and the Sea of ​​Oman were extracted. These values ​​as model input, using the third generation numerical model MIKE21 (SW) which simulates wave parameters with the least assumptions, were converted into wave characteristics and finally the amount of changes in wave characteristics such as wave height Wave period and wave direction have been obtained in the Persian Gulf, the Sea of ​​Oman and part of the Indian Ocean that is affected by climate change in the region. The results of studies have shown that the effect of climate change on wave height and wave period is negligible, but in Makran beaches the effect of climate change on the angle of the wave direction can be considered and this will affect the sedimentation process. The effect of climate change on the wave direction and its effect on the structure should be further investigated for the construction of ports and marine structures such as desalination catchments in these areas.