نشریه پژوهش های اقلیم شناسی
پیاپی 37-38 (بهار و تابستان 1398)

Recognizing the interactions of the large-scale components of atmospheric circulation creates an appropriate capacity to examine the regional climate variability and improve description of land-atmosphere connections. Assessment Iran vulnerability to climate fluctuations is very important. Part of this recognition will be achieved by assessing the components of the atmospheric circulation. The relative vorticity distribution is an important index of synoptic motion in mid latitudes. Regions of positive relative vorticity are associated with cyclonic storms in the Northern Hemisphere. Thus the distribution of relative vorticity is an excellent diagnostic for weather analysis. Vorticity, the microscopic measure of rotation in a fluid, is a vector field defined as the curl of velocity. Relative vorticity is a measure of the intensity and direction of spin in a circular movement, which is performed by a unit volume of air around the vertical axis perpendicular to the plane over which this rotation occurs. Relative vorticity is a good quantity for studying atmospheric changes, because it presents the main order of magnitude of daily cyclonicity or anticyclonicity.

Conclusion The present research, through applying precipitation and temperature extreme events, illustrates that percent of forecasted precipitation and temperature changes in comparison with the average base period of 1981-2010, in 2030, 2060, 2100 will increase procedurally. Spatial variability, and annual coefficient of variation in various regions are different. north, western north, eastern north and east will include the least temperature fluctuations, and the highest percent of precipitation with the highest coefficient of variation which conveys chronological period precipitation distribution with disordered accumulation and more local difference in this region in comparison with other regions. Then, Ghafghaz mountainous region has the highest percent of precipitation rise with suitable scattering in a year. The southern region of Caspian sea will experience the most rise of temperature and lowest percent of precipitation rise. High coefficient of variation in this area illustrates abnormal and disordered pattern on the threshold of precipitation. Sea level rise with three estimation regression, low average, high, on the basis of sea level ascending pattern equation For both scenarios, fluctuations in sea level based on subsidence Caspian pit seabed was calculated. In general, average annual sea level is increasing which is about 1.22 cm each year for scenario RCP8.5, and 0.93 cm yearly for scenario RCP4.5. Through this article, it can be found that changes in coastal region is unavoidable. However, inhabitants in this region have no system or not yet developed which can help them to adopt themselves with climate change issue. This study illustrated the significant effect of coastal climate which through climate change how society and economical activities are influenced.

In this paper, data of the annual fastest wind speed in Bushehr station in south of Iran were used and graphical and numerical methods were applied to compute scale and local parameters of the Gumbel Distribution Function (GDF). Then, different return periods for the annual fastest wind speed were estimated. In the estimation process of local and scale parameters, Standard analytical procedures such as Method of Moments (MOM), Method of Order Statistics Approach (OSA), Least Squares Method (LSM) and Maximum Likelihood Method (MLM), were used.  
Numerical computations show that the Method of Moments (MOM) provides better results compared to other methods and computed values for the scale and local parameters in estimation of annual fastest wind speed in Bushehr station are the best estimation.
Computations of the annual fastest wind speed for return periods of 25, 50,100 and 1000 years, estimated to 29.7 m/s, 32.8 m/s, 35.8 m/s and 45.9 m/s, respectively. Moreover, we can say that, in the confidence level of 95%, every 207.2 and 82.9 years, annual fastest wind speed of 39 m/s and 35 m/s can happen, respectively.

The solar energy is the source of energy on the planet, one of the most important sources of clean energy, especially in Iran. The correct estimation of solar radiation is considered one of the important parameters in simulating plant growth and the estimation of evapotranspiration is very important. Measuring the intensity of solar radiation, although it has a relatively long history in Iran, due to the high cost, most of the stations in the country are not equipped with a radiation meter, and this defect is even seen in developing countries. Since the prediction of solar radiation intensity in across Iran daily range with GeneXproTools software has not been done so far, due to the wide range of different climates in different regions of the country, this research has been carried out across Iran. Therefore, the ability of the simulation model of GeneXproTools software to predict solar radiation based on altitude, longitude, latitude, precipitation, relative humidity, maximum temperature, minimum temperature, average temperature and solar radiation on a daily basis in 31 stations over Iran. The period from 2007 to 2016 has been taken. Iran with an average annual rainfall of 241 mm is located in dry and semi-arid latitudes of the planet, between the two meridians of 44⁰ and 64⁰ in the east, and two circuits 25⁰ and 40⁰ in the north. About 94.84% of its surface is located in arid and semi-arid regions with low atmospheric rainfall and high evapotranspiration. Meteorological stations were selected based on climatic variation in this research. In this way, the study stations were divided into six submergence classification systems, hyper-arid, arid, semi-arid, Mediterranean, humid and very humid (A). Gene expression planning (GEP) is one of the newest methods of artificial intelligence. This is the generalized method of genetic algorithm (GA), which was based on Darwin's theory and invented by Ferreira in 1999 (Roshgar and Mirhidaryan, 2014). In order to compare the results of the gene expression algorithm were defined in the prediction of solar radiation, 8 scenarios are considered based on the parameters affecting solar radiation such as altitude (m), longitude (degree), latitude (degree), precipitation (mm) relative humidity (%), maximum temperature (°C), minimum temperature (°C) and average temperature (°C). The main objective of this research is to select the best model for predicting daily average solar radiation in Iran using meteorological parameters. The number of training and testing data for the GEP model is presented in Table 1. Among the data collected, 80% of the data were used for training (total 8000 data, 6400 data from every parameter for every 31 stations) for the model. The experiment was performed for 20% of the data (a total of 1600 data from every parameter for each 31 stations) for the model.
 
Table 1. Training and testing data for study stations for the GEP model


 
The performance of the GEP model by evaluating the best fitness fittings, namely RMSE, MAE, NSE and R2, which at best (Best Mode) of the values of best fitness RMSE, MAE, NSE and R2were 1000, 0, 0, 1, and 1, respectively. Comparing the results of model assessment statistics in different scenarios, it was found that in all scenarios, c scenario due to consideration of latitude (degree) parameters, precipitation (mm), average relative humidity (%), maximum temperature (°C), minimum temperature (°C), average temperature (°C), and average solar radiation (MJ.m-2.d-1), the desired prediction has presented. The best model for this study is R2 for training and data testing of 0.72 and 0.73, respectively. The GEP model shows the optimal output model as a tree, and also the equation derived from this structure. Since the four genes in this study are composed, each gene has a subtree and its own equation, which ultimately results from the final equation bond function. The final equation is a relation 1.
Rs= SUB ET1+ SUB ET2 + SUB ET3 + SUB ET4 = sin((((((2RH)-cos(Tmax))-cos(Tmean))-p)/altitude)) + Tmean + sin(altitude) + ((((altitude + 7.30)-(2 Tmean))-p)/((sin(-8.41)+ 5.76)+atan(Tmin)))) From the results of the analysis, it was found that the predicted values ​​are well adapted to the values ​​measured in the Gene Expression Programming Model (GEP). In the GEP model, the e scenario is due to the fact that it does not consider altitude (degree), longitude (degree), latitude (degree), precipitation (mm), relative humidity (%), maximum temperature (°C) and minimum temperature (°C), with a lower correlation coefficient and more error and less efficiency. In this model, in the c scenario, with the addition of average temperature, and solar radiation parameters, a more favorable estimation of solar radiation is obtained. The results of the c and e scenarios are very close together, but the e scenario is weaker in the prediction of solar radiation, and the best model in this study is c scenario. More precise results will be obtained when latitude (degree), precipitation (mm), relative humidity (%), maximum temperature (°C), minimum temperature (°C), average temperature (°C) and solar radiation (MJ.m-2.d-1) are predicted for solar radiation. Also, the criteria for assessment under c scenario are higher with R2= 0.72, RMSE = 3.59, MAE = 2.82, and NSE = 0.72 in the education section of other scenarios. In general, the GEP method has the most accurate results in estimating daily solar radiation in Iran.

Rainfall erosivity is defined as the aggregative power of the rain. If other effective features on soil erosion be considered constant then soil loss could be directly connected to rainfall erosivity. Rain erosion term was proposed by Wichmeier and Smith in 1978 to consider the effect of climate on raw erosion. Measurements of meteorological parameters by the traditional methods require a dense rain gauge network. But, due to the topography and cost problems, it is not possible to create such a network in practice. Given the significant change in rainfall in time and space on the one hand and low rain-gauge stations to record rainfall on the other hand, the necessity of using geostatistical methods for rainfall erosivity mapping is inevitable. Geostatistical methods use the spatial correlation between observations in the estimation processes. In these cases the spatial distribution pattern of rainfall erosivity can be produced using different methods of interpolation. Study area Lorestan province is located in southwest of Iran and covers an area of 28249 square kilometers. It is located between the latitudes 32º 37' and 34º 22' N and the longitudes 46˚51ʹ and 50˚30ʹE. The main objective of this research were: (1) analyze the spatial distribution of rainfall erosivity using two different interpolation methods namely ordinary Kriging and simple Kriging; (2) put forward the best interpolation method through cross-validation, construct the high resolution grid data of rainfall erosivity and provide the reliable information for relevant researches
 In the first step, the precipitation data collected from 53 precipitation stations and Modified Fournier Index (MF) calculated based on Eq. (1)
                                                                                                (1)
 Where MF is the modified Fournier index value (mm), pi  is averagemonthly precipitation (mm) and P is average annual precipitation (mm). Then Eq. (2) and Eq. (3) were used to estimate rainfall erosivity or R-factor values (MJ mm ha -1 h -1 year -1).
 
It is suggested that Eq. (2) be applied for locations with MF values less than 55 mm and Eq. (3) be used for locations with MF values greater than 55 mm.
  In this article two interpolation techniques namely simple and ordinary Kriging were compared in GS+5.1.1 and ArcGIS10.3 software’s in order to determine which one describe better the spatial distribution of rainfall erosivity. Kriging methods assume that the spatial variation of a continuous climatic variable is too irregular to be modeled by a continuous mathematical function, and its spatial variation could be better predicted by a probabilistic surface. The predictions of Kriging-based methods are currently a weighted average of the data available at neighboring sampling points (weather stations). The weighting is chosen so that the calculation is not biased and the variance is minimal. A function that relates the spatial variance of the variable is determined using a semi-variogram model which indicates the semi variance between the climatic values at different spatial distances. The resulting maps from interpolation were compared by using a set of validation statistics include Mean error (ME), Mean Standardized Error (MSE) and the root mean square error (RMSE) by Eq. (4), (5) and (6)
  Based on the results, rainfall erosivity values varied from 11.1 to 749.5 MJmmha−1 h−1 y−1. Differences between the simple and ordinary Kriging models regarding the validation statistics were narrow, but allowed for a comparison. The obtained results showed that ordinary Kriging with higher R2 and lower ME, MSE and RMSE had better precision in mapping rainfall erosivity. The spatial distribution of rainfall erosivity showed the areas along north-south of Lorestan province and central regions had higher values while lower rainfall erosivity was seen in the western and eastern areas of the study area. The availability of high-quality environmental maps is a key issue for agricultural and hydrological management in many regions of the world.  Produced rainfall erosivity map in this research can be used for estimation of soil loss by USLE model. Rainfall erosivity maps also can be suitable as guidance for soil conservation practices and identifying areas with the high potential of soil retention as an ecosystem service. Further research may be directed to find reliable erosivity indices which can be computed from daily precipitation data.

The traditional approachof economics is based largely on the wasteful use of natural resources and the lack of attention to future generations' rights to these resources. However, the environmental impacts of such an approach will be irreparable and in order to achieve the sustainable development, it has to be changed. Considering the importance of protecting the environment as well as the need to pay more attention to the sustained growth and development, this study, examines the factors affecting air pollution from the economic and institutional perspectives with a greater focus on the two variables: trade openness and the control of corruption index. Researches in the field of environment confirm that there is an inverted U-form relationship between the economic growth and the air pollution. In fact, the Environmental Kuznets Curve (EKC) claims that in the first stage of development, pollution grows rapidly; because the rapid growth results in untapped use of natural resources, and therefore the air pollutants generation increases. But at the high levels of development, people value the healthy environment, more effective environmental regulations will be implemented, regulatory institutions become more effective, the economic structure moves toward clean industries, and finally the pollution level declines. Regarding the role of trade patterns in the transmission of pollution, from high income economies which have stringent environmental standards to the middle and low income economies that place high levels of production and employment as their top priorities, new models have been proposed that can cover the role of trade in the EKC. Two conflicting hypothesis emerge from the debate: the pollution haven hypothesis (PHH) and the factor endowment hypothesis (FEH). According to the pollution haven hypothesis, pollution control policy differences across countries drive pollution-intensive industries to the countries with weaker regulations. Another hypothesis, the factor endowment hypothesis (FEH), predicts that rich and developed countries will specialize in polluting goods. Since the abundance of capital supports the production of more capital goods and polluting industries. On the other hand, countries with a lot of workforce and land tend to be more specialized in less polluting sectors. Studies show that with the entry of commercial variables into the discussion, increasing or decreasing of pollution is different depending on the country and the environmental index which is used. It seems that only the entry of economic variables, is not a solution to the problem, because a growing attention of countries will be given to the condition of pollutants and the need to move toward a sustainable development. As a result, they will face the challenge of complying with environmental regulations. Concurrent importance of economic growth, and reducing poverty and unemployment has made some managers to ignore the neglecting of rules by companies. So they can also obtain benefits for themselves through the rents. In this regard, some recent studies have come up with models that apply the effects of institutional factors. Most of these researches have shown that improving the governance, and in particular controlling the level of financial and administrative corruption, play a crucial role in reducing emissions. However, it seems that the quality of the regulatory institutions that can ensure the correct implementation of environmental regulations is less considered in the existing literature. So, using the Fully Modified Least Squares (FMOLS) approach in selected countries of three income groups from 1996 to 2011, the purpose of this study is to investigate the effects of trade openness and control of corruption on the particulate matter index. Empirical results indicate that the effect of trade openness on air pollution in all three income groups is negative and significant. It means that increasing in trade openness will improve the air quality and reduce PM10 emissions. Furthermore, the empirical findings show that with an increase in control of corruption, there is a significant decrease in PM10 emissions in Middle and Low income groups; while this effect is not statistically significant in high income group. Therefore, reducing the level of corruption and combating of corrupt activities will enhance the air quality in Middle and Low income groups. Since the clean air and healthy environment play an important role in the survival of human beings, the results of this study can lead to policies for the sustainable development.

In Iran, precipitation is one of the key variables for assessing the potential of water resources access. But because the spatial distribution of this variable is very uneven, the distribution of water resources of the country is not uniform. The maintenance and management of water resources, while also being a function of rainfall, depends on the variability of precipitation. The smaller the spatial variation of the rainfall, the greater the homogeneity and consistency of water resources. On the other hand, the less variability of rainfall is, the more water resources will be more stable and water supply will be possible. For this reason, the variability of rainfall time in the assessment of water resources, watersheds and the relative study of local and regional water resources is important.      The data used in this study are available statistics for 60 years of daily precipitation of the synoptic station of Tabriz from 1951 to 2010, which was obtained from the Meteorological Office of East Azarbaijan Province. For accuracy raising the modeling stage, data is considered weekly. The most commonly used model used to show the time series of discrete random variables is known as the Markov chain. Examining these uncertain or random modes and selecting the model is the probability knowledge. In this research, it is attempted to use this knowledge and based on the Markov chain method, the probability of rainfall occurrence in Tabriz city is obtained.                                                                                               The distribution of rainfall during the year can have a large impact on water and agriculture planning. Considering the fact that Iran is located in the dry world belt and most of its regions have dry and semi-arid climates, and agriculture in these areas It is also based on this climate. Changes in rainfall can cause irreparable damage to the agriculture and water resources of these areas. Therefore, recognizing its system of changes can be a great help in this regard. The number of rainfall days is an appropriate criterion for assessing the distribution of rainfall. The average rainfall days in Tabriz average 80 days per year. An average of 30 days was observed in the spring, 5 days in the summer, 19 days in the fall and 26 days in winter.   Conclusion The study of state change matrix shows that during the statistical period (1951-2010), from the total of 21960 days of the studied statistics, 14649 days of change from dry day to the next dry day and 2519 days of change in the rainy day, which after dry day It happened. Also, the transformation matrix from rainy day to dry day is 2520 days and rainy day to rainy is 2272 days. The results of the X2 test indicate that the data are not independent and there is enough confidence to adhere to the daily precipitation data of Tabriz station from the Markov ranking model. The results of the trend test using Spearman correlation method at 95% confidence level indicate that the data are not trendy and validated by the approved chain. The results of the fitted final model showed that the probability of changing the order of the rainy day to the other rainy days has a higher percentage than the change from rainy day to dry, and this state is in the weeks leading to the spring and autumn seasons, More than other weeks.