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In this research, the most important synoptic patterns of widespread snowfall in the eastern half of Iran have been investigated. For this purpose, the current weather code data and snow depth of synoptic stations in the eastern half of the country during the statistical period of 1371-1400, for the months of October to March, were received from the country's meteorological organization. In order to investigate widespread snowfall, the days when more than 70% of the studied area saw snowfall at the same time were extracted as a widespread day. In order to perform synoptic-dynamic analysis of widespread snowfall in the eastern half of Iran, the classification method using cluster analysis was used and the maps of the representative days including atmospheric temperature, moisture flux, geopotential height, vorticity, front formation, jet stream, omega index and orbital and meridian wind data were drawn. Trend analysis was also performed using the Mann-Kendall test. The results showed that 3 patterns justify the snow cover in the studied area. These patterns are: high pressure in Siberia and central Europe-low pressure in eastern Iran, high pressure in western Iran-low pressure in Sudan, high pressure in central Europe-low pressure in eastern Iran and Afghanistan. In all the patterns in the middle of the atmosphere, the intensification of the meridian currents of the western winds along with the formation of high pressure and low-pressure centers has caused blocking in the path of the western currents and has provided the conditions for the ascent of the air. The concentration of the negative omega field and the relative positive advection, along with the location of the northeastern region of Iran in the left half of the outlet of the Subtropical Jet, have caused severe instabilities and widespread snowfall in the region. Also, the results showed that despite the absence of a trend in the number of snow days in the northeast of Iran, the number of snow days has decreased over time.
 

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There are various indicators to monitor and management of agricultural water resources in arid and semi-arid countries including Iran, some of which can be extracted directly in situ, and some can be retrieved using remote sensing technology and satellite images. Aim of this study is to propose the most appropriate and efficient indicators of agricultural water resource management for achieving maximum production and maximum water efficiency using remote sensing technology, therefore, Crop Water Stress Index (CWSI) and Surface Energy Balance Algorithm (SEBAL) were used to estimate Evapotranspiration (ET). In the first step, ET rate was calculated using SEBAL algorithm for six Landsat 8 satellite images related to the wheat growth period. Then, zoning of this index was done in the range of zero to one, in four categories of very low, low, medium and high, which respectively indicate the lowest to the highest amount of ET. In next step, CWSI was calculated based on Idso equation, and its results show different changes both in cold season and in warm months. Comparison of ET and CWSI shows a significant relationship between these two indices in warm months, while in cold months, no significant relationship can be seen. These findings along with the established relationship between ET and CWSI can inform water management strategies in arid environments for sustainable crop production.

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Estimating and predicting precipitation and achieving its runoff play an important role to correct management and exploitation of basins, management of dams and reservoirs, minimizing the flood damages and droughts, and water resource management, so they are considered by hydrologists. The appropriate performance of intelligent models leads researchers to use them for predicting hydrological phenomena more and more. Therefore, in this study, the Gene Expression Programming (GEP) and Support Vector Regression (SVR) models were used to model monthly precipitation of Nahavand City. In this study, precipitation, temperature, and relative humidity data were used in a 32-year period (from 1983 to 2014). The results showed that the same and good performance of both models (R²= 0.92), but according to different evaluation criteria, GEP model showed a little better performance (RMSE= 0.0478 and 0.0486), while the running GEP model is so easier than the SVM model. Totally, it can be said that GEP model had been suitable for modeling monthly precipitation of Varayeneh station in Nahavand City. Finally, the monthly precipitation was predicted the GEP which showed a decrease in precipitation in compared with previous months.
 

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Abstract
In recent years, attention has been paid to climate change, which could be the result of economic, social, and financial losses associated with extreme weather events. The purpose of this study is to investigate the variation of extreme temperature and precipitation in Kurdistan province. For this purpose, daily rainfall data, minimum temperature and maximum temperature of 6 stations were used during the statistical period (1990-1990). And their changes during the period (2041-2060) using the universal HadGEM2 model under two scenarios RCP2.6 and RCP8.5 and the LARS-WG6 statistical downscaling were investigated. In order to study the trend of climatic extreme indexes, rainfall and temperature indices were analyzed using RClimdex software. The results showed that during the period (2016-1990), hot extreme indicators have a positive and incremental trend. This trend is significant for the "number of summer days" and "maximum monthly of maximum daily temperature" indicators. This is while the cold extreme indexes had a decreasing and negative trend. This trend was significant only for the "cold days" index. Extreme precipitation in Kurdistan province has a negative trend in most stations. ،this trend is significant at most stations, that indicates a reduction in the severity, duration and frequency of precipitation during the study period. The results of the climate change outlook also indicate that the temperature will increase over the next period and rainfall will decrease.
 

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