tayyebeh mesbahzadeh

The increase in greenhouse gases in recent decades and the resulting increase in temperature have upset the balance of the Earth's climate system and caused widespread climate change in most parts of the world. According to the definition of the Intergovernmental Panel on Climate Change (IPCC) (2013), climate change is a change in the state of a climate in which its mean or changeable characteristics change for decades or longer. Therefore, forecasting climate variables in the future is necessary to consider measures to reduce the effects of climate change. Climate models are good tools for assessing the climate in the future. These models simulate climatic parameters in the future by different scenarios of radiation induction. Selecting an appropriate climate model is essential for predicting and simulating climate data in the future, as well as evaluating changes in climate parameters in the future. An overview of studies conducted on the application of climate change models in the country indicates that in most studies, the application of atmospheric circulation models has been studied and regional models have been evaluated less. Therefore, in this study, two models of general circulation of atmosphere and region in Isfahan province are compared.

Isfahan province, with an area of 107018 square kilometers, is located between 30 degrees and 43 minutes to 34 degrees and 37 minutes north latitude of the equator and 49 degrees and 38 minutes to 55 degrees and 32 minutes east longitude of the Greenwich meridian. The climate of Isfahan province is generally temperate and dry. In this research, daily observational data including rainfall, average temperature and average wind speed from the Meteorological Organization, data predicted by REMO model in the base period 1991-2005 and the future period 2020-2100 for the parameters of rainfall, average temperature and wind speed from the base CORDEX data and three types of inputs were used to model climate variables by CanESM2 model. These inputs include daily precipitation data, mean temperature and mean wind speed of the synoptic station, open atmosphere analysis data (NCEP) and CanESM2 model data. To evaluate the efficiency of the models in the study area, the observational data and the data predicted by the models in the common statistical period by the statistical criteria of Percentage coefficient (PBIAS), Nash-Sutcliffe coefficient (NSE), standard ratio root (RSR), explanation coefficient (R2) and square error (RMSE) were assessed. Also, in order to better evaluate the performance of the models, the data simulated by the models were compared with the observational data by months.

The results of this study for the REMO regional climate model showed that this model is not suitable for simulating the rainfall parameter in Isfahan, Airport, Kashan and Golpayegan stations, nor is it suitable for simulating the average temperature parameter in Isfahan, Golpayegan, Nain and Kashan stations. In these stations, on average, the values simulated by the model for the precipitation parameter are more than the observational values and for the average temperature parameter are less than the observational values. In other studied stations, the values simulated by the model for rainfall parameters and average temperature have an acceptable agreement with the observational data. Also, the correlation of the simulated values of the model with the observational data for the wind parameter indicates that this model is not suitable for predicting wind data in the study area and the values simulated by the model are on average higher than the observational data in the period. The results of the CanESM2 atmospheric circulation model showed that the SDSM model has a high efficiency for modeling and exponential microscale in the study area. The results also showed that this model has a high efficiency for simulating the parameters of average temperature and wind speed relative to rainfall in the study area, which can be due to the presence of zero values in the data series. The results of rainfall parameter statistical criteria showed that the CanESM2 model in Daran, Isfahan, Khor and Biabank, Nain and Natanz stations is suitable for simulating rainfall in the future. Also, the results of the statistical criteria and simulated data of the model in the joint period for temperature and wind parameters showed high agreement with the observational data.

Climate models are appropriate tools for assessing the climate in the future. In this study, the efficiency of REMO regional climate models and the general CanESM2 atmospheric model were investigated. The results showed that the REMO climate model does not have the appropriate efficiency to simulate rainfall, average and wind speed in the study area, but CanESM climate model has high efficiency to simulate average and wind speed in the region. It can therefore be used as a suitable model for forecasting future climate in the study area.

Precipitation plays an important role in climatic, water, energy and biogeochemical cycles. Several global and regional data sets currently provide historical estimates of this variable over Iran, including the MWEP and WFDEI forcing datasets and production of some institutions such as MOHC, SMHI and IITM. All these datasets provide data with different resolutions based on gage stations, satellite Images and models output. In this study, we do an inter comparison between these data sets during 1990- 2008. We also validate all ten data sets against independent ground station observations over 30 second-order basins of Iran. MSWEP and WFDEI have an acceptable compatibility with observational data on different spatial and temporal resolutions. RMSE and Bias are 5.68, 6.34 and 0.58, 2.75 for these two datasets during 228 months, respectively. However, it is needed that MSWEP improves in the western and northwestern parts of the country and WFDEI in June and September months. Our findings in this research provide valuable guidance for a variety of stakeholders, including rainfall- runoff and land-surface modelers, watershed management studies and data providers.

Flood damage assessment is often necessary for early flood management. To this end, this paper provides a framework of rapid estimation of flood damage and identification the flooded areas in March 2020 using Sentinel-1 satellite data. To this end, in the present study, after applying the necessary pre-processing in SNAP6 software, the backscattering coefficient, or sigma naught for two images related to before and after the flood occurrence was extracted. The backscattering coefficient histogram was used to separate the image into two classes including water and non-water and the threshold of 0.01 was obtained based on it. Then, by applying mathematical operations on both backscattering images, the binary image of water and non-water was prepared and the flooded areas were determined based on the difference between the two images. After detecting the flooded areas, Sentinel images were classified into three classes including waterbody before flood, flooded area and other lands using supervised classification algorithms. The results indicated the high accuracy of the Random Forest algorithm with kappa of 0.92 compared to other algorithms. By overlaying the land use and flooded areas maps, the inundation percentage for each land use was determined. According to the results, bare lands with 27.9 percent, residential land with 16 percent and rangelands with 12 percent had the highest inundation percentage, respectively.

Global warming has occurred in recent decades, and its warming trend will continue due to increasing concentrations of atmospheric greenhouse gases from human activities. Changes in the average rainfall in many areas have also been recorded in connection with global warming due to rising atmospheric humidity. Climate change is another threat to the security of water resources. Therefore, climate change has a significant impact on the hydrological cycle on a global and local scale. The issue of understanding climate change is of particular importance for areas dependent on seasonal water availability and areas exposed to vulnerable climates. On the other hand, Iranchr('39')s geographical location in the subtropical region has caused it to be located in the dry belt of the world. Due to this geographical location, the average rainfall in Iran (255 mm) is much lower than the global average (860 mm). A noteworthy point about Iranchr('39')s rainfall, in addition to low rainfall, is its high variability. On the other hand, in the last century, global climate change has been reflected in increasing temperatures, increasing precipitation changes, and decreasing glacial and snow regions.

This study was conducted in the southern region of Kerman. In this study, the first daily temperature and rainfall data of Jiroft and Kahnooj synoptic stations were prepared by the Kerman Meteorological Organization to investigate climate change during the period (1989-2017). After collecting data, SDSM4.5 software was used to study climate change and the trend of these changes.

The results of the performance of the CanESM2 model for its ability to microscale exponential parameters of precipitation and average temperature during the base period indicate that this model has a high capability in simulating climate change in the south of Kerman province for the period (2050-2021 and 2080-2021) .According to the exponential microscale results, the average temperature has the best performance relative to the daily precipitation, which can be one of the reasons that the temperature changes relative to the precipitation change over a period of time, while the precipitation parameter fluctuates widely. According to the three evaluation criteria of NSE, RMSE, and R2, the performance of the CanESM2 model is in a very good and good class. The results show that the temperature changes in both periods and both stations are incremental. However, the changes in rainfall in each of the scenarios did not follow a specific rule and are increasing and decreasing.The results show that the average temperature during the period (2050-2021) in Jiroft synoptic station indicates that under the scenarios of RCP2.6, RCP4.5, and RCP8.5, respectively, 1.63, 1.61 and It rises to 2.06 ° C. These temperature changes in the period (2080-2051) are also positive. Following the mentioned scenarios, the temperature will increase by 2.02, 2.88, and 4.23 ° C. The results of temperature change and precipitation in the base period (1989-2017) showed that in the base period for synoptic stations of Jiroft and Kahnooj, the temperature parameter due to the P-value value is less than 0.05, which means that this trend also includes Kendallchr('39')s tau and age line slope. The trend of temperature changes in the future in both stations is significant and indicates an increase in temperature in the future. Also, changes in precipitation in the base period in Jiroft synoptic station are without trend.On the other hand, these changes in Kahnooj synoptic station are significant and decreasing. In the next period, rainfall in Jiroft synoptic station is without trend. The results also show that only in the period (2080-2051) in the RCP4.5 scenario in Kahnooj station, its precipitation trend is significant and decreasing.

In the present study, to study climate change in the south of Kerman province, the model of the fifth report of the International Board of Climate Change CanESM2 and new release scenarios in the next two periods (2080-2051 and 2050-2021) were used. Also, to clarify the trend of annual changes in temperature and precipitation components as inputs of hydrological models, a non-parametric Mann-Kendall test was used. The results of evaluating the accuracy of the CanESM2 model showed that this model has good accuracy in both temperature and precipitation components in both synoptic stations.. On the other hand, the trend of temperature changes in the future indicates that temperature changes have had an increasing trend.

Today, the phenomenon of dust is known as one of the most important natural disasters in arid and semi-arid regions. The long-term effects of this phenomenon on the human health index are referred to as chronic disease. Therefore, studying and identifying the patterns and centers of this phenomenon seems necessary in these areas. In this study, in order to simulate the dust emission flux to determine the internal and external critical centers in the central plateau of Iran, WRF-Chem model and GOCART wind erosion scheme and storm were used from July 19 to 21, 2015. The results showed that the Arabian deserts in Saudi Arabia, the deserts of Iraq, as well as the Gharegham desert in Turkmenistan and the Helmand region in Afghanistan are among the most important foreign crisis centers affecting Iran's central plateau atmosphere. Also, the Central Desert (Dasht-e Kavir) has been identified as the main source of dust and the southern parts of the Central Loot Basin and the Jazmourian Basin have been identified as the internal sources of dust. The results also showed that in the Central Loot basin, the amount of 6900 micrograms per square meter of dust increases per second due to the erosion conditions.

Dust has a significant impact on radiation budget, global biogeochemical cycles, soil structure, atmospheric chemical composition, air quality and public health. Natural and human factors have increased the frequency and severity of dust storms in the country in recent years. The most important natural factors are rainfall, drought persistence and global warming, and human factors include surface water control, agricultural development and uncontrolled groundwater consumption, land degradation and excessive use of climatic and ecological potential of the region. These factors can lead to the development of dust centers and increase their activity.

This study was conducted in Central Iran, where one of the most important environmental problems in this region is the occurrence of destructive dust storms. Arid and semi-arid climate, bare soil with sparse vegetation and severe storms are the characteristics of the central region of Iran that prepare the conditions for the occurrence of dust storms. The provinces of Kerman, Yazd, Isfahan, western parts of Sistan and Baluchestan, Semnan province, Khorasan Razavi are directly affected by the consequences of these events. In order to analyze the frequency of dust days, dust codes taken from 37 synoptic meteorological stations with longer statistics were used. For each station, the number of dust days on an annual scale in the statistical period 1999 to 2018 was extracted. In order to analyze the frequency of dust days, Professional5 Easyfit statistical software was used, based on which the most appropriate statistical distribution function for each synoptic station was selected. EasyFit Professional software fits data into 61 distribution functions. Then, using the Kolmogorov-Smirnov test, the most appropriate probability distribution function was determined.

The results show that the statistical distribution of Johnson SB was found to have the highest frequency in the study area. The Log-Logistic (3P) statistical distribution ranks next. A number of stations such as Bam, Kerman, Nain, Arak, Saveh, Mahallat, Zabol, Semnan, Qom, Zahedan, Yazd and Tabas can have more than 100 days of dust in the return period of 50 or 100 years. Also, Tabas, Qom, Yazd, Zabol, Zahedan and Arak stations show at least 50 days of dust every year. The zoning of dust days with a return period of 5 years also shows that the southeastern parts of the study area, as well as areas in the center and northwest of the basin, can be identified as sources of internal dust. The zoning of dust days with a return period of 25 years shows that the southeastern parts of the study area below the Loot basin and also the northeastern parts of the study area below the central desert basin (desert plain) can have a maximum of 225 dust days in the return period. The results of frequency analysis of dust days with a return period of 50 years show that in some areas of the study area, more than 276 days of dust per year can occur and these areas are identified as dust sources in the study area.

In recent years, dust events in the West Asian region have occurred with greater frequency and intensity. Dust phenomenon in Sistan and Baluchestan, Kerman, Yazd, Isfahan, Semnan and Khorasan Razavi provinces is one of the most important challenges.  Identifying and predicting dust source in these areas are of great importance.One of the most important forecasting methods in different return periods is the use of statistical distributions. In this study, using 61 statistical distributions and Easyfit Professional 5.5 software, the most appropriate statistical distributions were determined using the Kolmogorov-Smirnov test, the most appropriate probability distribution function in the study area. Johnson SB statistical distribution was found to have the highest frequency in the study area; also, Log-Logistic (3P) statistical distribution is in the next rank. Then, using statistical distributions to identify indicator stations, the number of dust days in the return period was estimated from 2 to 100. The results showed that Bam, Kerman, Naein, Arak, Saveh, Mahallat, Zabol, Semnan, Qom, Zahedan, Khoor and Biabanak, Yazd and Tabas stations according to the analysis of the frequency and number of dust days estimated in the annual return period to Index station were identified in the study area. Also, changes in the spatial pattern of dust in the return periods of 5, 25 and 50 showed that the southeastern parts of the study area of Kerman, Sistan and Baluchestan provinces due to the proximity of Hamoon and Jazmourian wetlands and 120-day winds of Sistan as one of the dust centers have been identified.

Soil quality, as a factor, is changing due to variation of soil inherent characteristics and the type of management practices on the soil. Integrated Quality Index and Nemero Quality Index were applied to evaluate the influence of land use types, different irrigation managements, and types of cultivated products on the soil quality in Semnan region.  After describing 13 soil profiles in five selected crop fields and orchards, soil samples were taken from different horizons and 14 properties efective on the soil quality were determined and the proposed indecs were calculated. The results showed that the quality of top layers of agricultural lands are classified as II and III degree, while the top layer and sublayer of garden lands are classified as IV degree due to lack of profile evolution, low organic carbon and high salinity. The highest average soil quality indices were assigned to alfalfa (flood irrigation) with 0.67; followed by barley (sprinkler irrigation) with 0.59, olive orchard (drip irrigation) with 0.39 and olive orchard (flood irrigation) with 0.32. This finding indicates the positive effect of cover crops on land reclamation of Semnan region. The effect of irrigation system on soil quality indices did not show any significant difference among them, as the flood irrigation in alfalfa and olive farms showed the highest and the lowest soil quality indecs, respectively. Also, there was no significant difference in soil quality of olive orchards under drip irrigation vs. flood irrigation. Soil quality indecs make opportunity to agricultural managers to choose appropriate management strategies and monitoring the changes in the soil quality.

The sustainability of soil, especially in fragile ecosystems, is an indispensable indicator of sustainable land management that depends on the physical, chemical and biological properties of the soil. In order to study the soil sustainability in Semnan desert region of farms, gardens and rangelands as arenas located on an educational farm with different irrigation administrations, the Sustainability Index (SI) and the Cumulative Ranting (CR9 &CR11) were used. Exploring the crust and sampling of surface and subsurface horizons of selected areas and measuring 12 important and effective characteristics of soil physical properties, the indices were calculated in each area and the effects of different types of land use on soil stability were investigated. The results showed that the SI in the surface layer of arable land due to the impact of cultivation and the positive role of organic matter was above one, and in the rest of the land was less than one which indicating the instability of the soil. According to the CR results, surface and subsurface layers of soil were not found in very stable and stable classes in the studied lands. The CR results also showed that the stability soil in the multi cultivates farm is a better condition, it may be due to its positive role in increasing soil correction factors compared to other lands. Correlation analysis of the indices showed that soil organic carbon, structural index, electrical conductivity, sodium adsorption ratio and mean weight diameter have a direct and more effective impact on soil sustainability.

Introduction
According to the IPCC (2014) definition, climate change is a change in the state of the climate in which the average or its modifiable properties varies for a long period. To date various versions of climate change models have been presented for different purposes. These models are including (1) the assessment models of the Intergovernmental Panel on Climate Change (FAR), (2) the models entitled SAR, (3) the report model of TAR, and (4) the model report titled AR4 (CMIP3) and, finally, the 5th Assessment Report models, AR5 (CMIP5). These models use new emission scenarios called "RCP". These scenarios have four key lines called RCP2.6, RCP4.5, RCP6 and RCP8.5. General circulation models are considered as the most reliable tools for simulating climatic variables. These models can simulate the present climate and illustrate the conditions of the future climate under specific scenarios. Although these models are very helpful in the investigation and predictions based on future changes in climate, the outputs of these models are based on a large grid scale (250 to 600 km). Therefore, the application of these models is not suitable on a regional or local scale. The most important tool to create a bridge between a regional/local scale and GCM scales is downscaling. Among statistical downscaling methods, SDSM has been widely used for the downscaling of climate variables in the world. Most of the studies on climate change have been used in AR4 models. The majority of the studies in the word used AR5 models to investigate changes in climatic variables. As mentioned, most of the studies about climate change modeling have been performed using AR4 models. Therefore, studying and updating of that with CMIP5 data is necessary to reduce the uncertainty of modeling climate change in recent decades. Thus, in this study, three variables of rainfall, averaged temperature and maximum wind speed are modeled using AR5 models. These parameters are modeled according to the basic period of 1961-2005 and the future climate change will be simulated in a 95-year period from 2006 to 2100. The investigation on changes of the maximum wind speed in this region, as windy areas, is affected by dust storms every year. This can be of great importance in the studies of dust and wind erosion in the future.
Material and In this study, we use three types of data including daily rainfall, average temperature and maximum wind speed from synoptic station. Second sources of data are NCEP variables and the third are CanESM2 outputs.
For analysis of General Circulation Model, we used in this study the second generation of Canadian Earth System Model (CanESM2) developed by Canadian Centre for Climate Modeling and Analysis (CCCma) of Environment Canada. This is the only model that made daily predictor variables available to be directly fed into SDSM. Also this model has three scenarios such as RCP2.6, RCP4.5 and RCP8.5.
The model SDSM has four main parts including identification of predictors, model calibration, weather generator and generation of future series of climate variables. After reviewing data quality control, predictive variables were determined by NCEP. Calibration of the model was carried out using 70% of the observational data during the statistical period of 1961-1992 to determine the coefficients of the equation for modeling rainfall data, temperature and wind speed. The coefficients obtained in the calibration phase were used for 30% of the remaining data (1993-2005) for model verification. The performance of the SDSM was evaluated based on comparison of the results of verification and observational data in 1993-2005. The performance of the model on downscaling of temperature is higher than rainfall and wind speed. The reason for this is that the temperature parameter is continuous variable and there are no zero values.
The results of evaluation of performance and model uncertainty showed that NSE, RMSE, R2, PBIAS, RSR and Pearson correlation coefficient for mean temperature were 0.96, 1.64, 0.96, 1.63, 0.18, and 0.97 based on downscaled data by the NCEP and 0.88, 1.3, 0.91, 6.8, 0.33, 0.49 based on the downscaled data of CanESM2, which is of a fairly good value. The assessment criteria of rainfall and wind speed are less than the average temperature, which can be explained by the fact that the rainfall data and wind speed are not normal due to the presence of zero values. Based on the results of the Man-Kendall test, observation and modeled rainfall under two scenarios of RCP2.6 and RCP8.5 have no significant trend. While the data generated by the RCP4.5 scenario shows a significant decreasing trend. The results of the Mean-Kendall test on average showed that the series modeled by RCP scenarios, as well as observational data, have a significant incremental trend. Incremental trend of temperature indicates the existence of climate change in the study area. The maximum wind speed showed that the observation and modeled data by RCP2.6 and RCP4.5 scenarios had a significant decreasing trend, while the generated data based on the RCP8.5 scenario had an increasing trend. Based on the results of CanESM2 model and SDSM, study area is not excluded from the climate change phenomenon. The trend model predicted an increase in average temperature in future under three RCP scenarios. The average temperature will increase by 1.54 degrees. According to the RCP2.6 scenario, the average temperature is 4.5 degrees, the scenario RCP4.5 is 7.7 degrees and the RCP8.5 scenario is 18.12 degrees higher than the base period. Unlike temperature variable, there is no significant change pattern for rainfall. According to RCP2.6 and RCP8.5 scenarios, rainfall will be increased in the future, while under RCP4.5 scenario, it will be decreased. The maximum wind speed will be increased by an average of 0.53 m/s compared with the base period. The RCP2.6 scenario is 4.9%, the scenario RCP4.5 44.4%, and the scenario RCP8.5 53.5% of increase compared with observational data

By identifying the removal areas the reasons can be identified instead of addressing the causes, And focused on executive activities in the harvesting areas And for this, the identification of sediments is particular importance In this paper, with the aim of better understanding and interpreting sedimentary samples, Investigating and analyzing the distribution of sediments in the facies of the study area has been investigated. For this purpose, after sampling of surface soil and preparation of samples, The drying procedure was performed according to A.S.T.M standard in 8 classes, which was smaller than 64 microns to 4000 microns. By entering the data into Gradistat software, statistical parameters such as mid-diameter, skewness and sample sorting were calculated based on the Fulc's comprehensive drawing method. The results of the study showed that the particle sorting is between 0.8 and 0.3, which confirms the near-average spacing distance from the harvesting area to the sediment accumulation point. The results of the particle tilting index are in the median vein facies, agricultural lands, agriculture, and sandy areas with symmetrical pebble cover. In other facies, the index is tilted towards fine particles. In the facies of the Rigi plain, the puffy lands and the permafrost shells of the wear coefficient classes are between 0-200 and then fully angled and the particles are transported from a distance. In the rest of the facies, the wear coefficients are between 200 to 400, in which case the particles are semi-angled and the particles are transported from a relatively distant distance.

Dust phenomenon is one the problem that is developed due to human interference and unreasonable utilities from natural resources and their destruction. In this review, the dust phenomenon frequency was done using selected synoptic stations statistical in Alborz and Qazvin provinces during 2000 to 2014 that annual frequency was evaluated by extraction of dust codes from meteorological data statistically. In order to study of velocity and direction of region winds, wind rose and storm rose of Karaj and Qazvin stations were drew and prevalent wind direction was determined that it showed prevalent wind is northwest and southeast at Karaj and Qazvin stations respectively. Study of the effect of drought on dust phenomenon event was done using SPI, PNI, ZSI indicator for annual scale and was evaluated via SPSS software. The drought result show that 2013 was the driest year at Karaj station and the SPI, PNI and ZSI values were obtained -1.64, 58 and -1.39 respectively. Also 2008 was the driest year at Qazvin station and SPI, PNI and ZSI values were calculated -2.34, 50 and -1.99 respectively. With studying of annual dust frequency with regard to 06 and 07 codes detected that totally 169 days was reported as day with dust at Karaj station that 2012 and 2014 had the most event frequency. Furthermore, totally 115 days with dust accrued at Qazvin station that 2011 with 23 days had the most event frequency.

Due to incorrect policies in the water resources sector and excessive withdrawal of groundwater resources, water quality in most plains of the country has decreased. Therefore, the quality of water for drinking and agriculture is important in qualitative terms.With this aim in this study, the desertification changes in Khash plain were studied using two criteria of groundwater and vegetation. Shooler and Wilcox diagrams were drawn up for AquaChem software for the years of 2001, 2008 and 2015. Also, spatial variations were made on Arctic plain surface using Arc GIS software and vegetation changes trend were also done using MOD13A2 sensor images and remote sensing techniques. The results showed that the number of wells that have an unpredictable TDS in the years 2008 and 2015 compared to 2001 have been increased by comparing the Schuler diagram. However, the level of lands with good drinking status decreased during the years 1387 and 1394 compared to 1380 and was increased by the level of acceptable drinking water. According to Wilcox's diagram, the result is that most wells are salty to very salty and their use is harmful to agriculture or requires measures. The results of the vegetation change trend also indicated that the trend is to reduce vegetation and the average level of the NDVI index decreases with the passage of time.

Statistical investigation of dust phenomenon in Alborz and Qazvin provinces Dust is a phenomenon that mainly occurs on arid and semiarid regions due to the high wind speed and its turbulence on soil surface of without any cover and talent to erosion (Khoshhal Dastjerdi et al., 2012). Goudie et al. (2009) point out that Sahara Desert are the strongest sources of global dust in West Africa and western China. Kumar et al. (2015) investigated three major dust events in 2010 that occurred in northern India during the monsoon season, by land measurements, satellite, and model estimation. The results showed that all dust storms originating from the desert or transported from it were affected by favorable conditions. In this paper, the annual and monthly frequency of dust phenomena in Qazvin and Alborz provinces has been studied during the statistical period in order to minimize the damage caused by this phenomenon using its results.
Material & Study area
Alborz province is located in the middle part of the Alborz Mountains that is adjacent to the north of Mazandaran province and south with the central province of the east with Tehran province and west to Qazvin province. For statistical study of dust was collected dust daily data in special codes for period of 15 years (2000-2014) from 7 selected stations in study area with suitable statistical period from meteorological organization that were processed using statistical methods. The extraction of days with dust was carried out using weather codes of 06 and 07 using MATLAB software. code of 06 is the dust is suspended in the air that is caused by soil and sand storm from distant points to the station and code of 07 is the dust or sands that raises by wind at station or near it in observation time. In this research, monthly and annual frequency of days with dust with visibility less than 10 km was investigated and separated with regard to codes of 06 and 07. Discussion of result: The annual survey of dust phenomena
The annual frequency of occurrence of days with dust showed that has occurred 549 days with dust according to codes of 06 and 07 for selected station in Alborz and Qazvin provinces. Karaj and Qazvin stations with 169 days and 115 days had the most day with dust respectively. Karaj station had most dust with 25 days in 2012 and 24 days in 2014 and there not have been dust in 2001 that annual average of dust was 11 days. Qazvin station had the most dust day with dust with 23 days in 2011 and there was not dust in 2002 and 2007 and the annual average of dust was 7 days.
The frequency of days with dust with breakdown of 06 and 07 codes showed that the most days with dust according to 06 code has been occurred with 104 days at Karaj station and the lowest dust events have occurred with 36 days at Avaj station. Karaj station has been the most dust events with 22 days in 2014 and the annual average of dust days was 7 days. Qazvin station totally had 91 days dust events that occurred the most dust occurrence with 22 days in 2011 and there was not dust in 2002, 2004 and 2007. Taleghan station had the most dust events with frequency of 19 days in 2012 and the lowest dust events with 6 days in 2014. Also, the average of days with dust was 11 days that was higher than other stations. Karaj and Qazvin station had the most frequency of dust events with 69 days and 27 days respectively.
The annual survey of dust phenomena
The monthly dust survey showed that have been occurred the most frequency of dust events in May with 116s and June with 115 days in selected stations in the statistical period of 2000 to 2014. May had the most dust with 32 days at Qazvin stations and June had the most dust with 30 days at Karaj station. Then, April with 79 days and July with 70 days had the most dust respectively that in April, Qazvin station had the most with 18 days and Karaj station had the most dust with 23 days and November and December had the lowest dust with 9 days. The monthly survey of dust phenomenon according to 06 code showed that May with 90 events of dust and June with 85 events of dust had the most frequency of dust events at selected stations in the study period respectively. The monthly survey according to 07 code showed that June and October with 15 incidences had the most dust that with regard to it Karaj station with frequency of 13 days in June and 7 days in October had the most dust. December and November with 1 and 2 days had the lowest frequency of dust occurrence respectively, and January according to 07 code had not dust during statistical period. The study of annual frequency of days with dust according to 06 code showed that the most days with dust occurred at Karaj station and the lowest dust events occurred at Avaj station that Karaj station had the most dust incidence with 22 days in 2014. According to 07 code, Karaj and Qazvin stations had the most events of dust and Buin Zahra and Avaj had the lowest events of dust respectively. With the monthly investigation of dust based on 06 code was found May and June had the most frequency of dust event that with regard to it, Qazvin station had the highest events in May with 30 days and June with 19 days. According to 07 code June and October had the highest incidence of dust that with regard to it, Karaj station had the most dust with 13 days frequency in June and 7 days in October. In general, based on the results, it was found that in the study area, with the onset of the spring season and the warm period followed by the summer season, the frequency of days with dust has increased, which can be due to increased hours of daily sunlight, superheating, and also There are some local instabilities. Another important factor is the passage of waves of the western masses from the deserts of neighboring countries, which, due to the dryness of the air flow and desert environment in those regions, causes dust and its penetration into the central parts of Iran. Movahedi et al. (2014) monitored the dust-related climate-related phenomena according to dust-weather codes for Iranian cities, and concluded that the dust phenomenon with the 06-meteorological code in western Iran has a maximum incidence, and in the season Summer is the highest and in the fall, is the lowest amount of dust. Also, incidence of dust occurred according to 07 code in the befor afternoon in the east of the Iran and occurred in the southeast and the coastal area of Oman sea in the late afternoon. Therefore, the findings of this study are consistent with some of the results of this study. According to the results obtained, it can be concluded that by examining the time and place of the occurrence of dust phenomena, it is possible to reduce the many damage caused by this phenomenon that threatens the environment and human health. It is very difficult to control dusts of external origin, mainly from the countries of Iraq and Syria, so it is suggested that, for the accurate analysis of internal dust, factors such as drought and use change of the factors affecting the occurrence of dust are to be considered.

 Today, desertification is one of the most important natural phenomena that losses sometimes "irreparably affect human life. This phenomenon will gradually capture its surroundings and cause impoverishment and migration of affected areas." Assessment and Monitoring this natural phenomenon is one of the basic plans of regional planners to deal with it. Monitoring this phenomenon in Iran, which has more than two thirds of it in the dry and semi-arid region and has fragile ecosystems, is of paramount importance. Due to this, the IMDPA's native model was developed for its evaluation. In this study, for the purpose of the evaluation of the desertification intensity of Kerman plain was based on the IMDPA model with emphasis on water and climate criteria. The results showed that the climate class desertification class did not change during the ten-year statistical period (2003-2013) and is in the middle class and also the class Meanwhile, desertification was determined, and the results showed that in the water criterion the area of low desertification intensity was reduced and the average desertification class area was increased and eventually it became clear that with the passage of time, the desertification conditions of the region became worse, even the desert class Severe growth has also been observed in some areas, especially in the western and southeastern parts of Kerman plain.

Soil moisture, as the soil hydrologic parameters, can be affected by soil temperature and controls various hydrological processes. Given the importance of this issue, in this study, the efficiency of artificial neural network was studied to simulate soil temperature at 5- 100 cm depth. Recorded meteorological parameters in the Isfahan synoptic station were used to simulate the soil temperature at different depths. The structure of the neural network was formed with an input layer, a hidden layer and an output layer and network training was done by Levenberg–Marquardt algorithm. Also test and error was done to determine a number of suitable neurons in hidden layer. The results showed that error in both neural network and ANFIS model increases with depth increase that can be due to the weak correlation between soil temperature changes in the lower layers and climatic parameters.

Dust storm is a phenomenon that mainly occurs in arid and semiarid regions in the result of high speed of wind and its turbulence on soil surface without coverage and erosion competent and cause to view amount reduction to less than 1 kilometer. In this study, dust and precipitation data in synoptic station selected in Alborz and Qazvin provinces was used during 2000 to 2014. After 06 and 07 codes extraction from meteorology data, days with dust were detected and monthly and yearly frequency of dust were analyzed statistically. evaluation of drought effect on the dust phenomena occurrence was performed using the SPI method for annual and monthly scales at 3, 9 and 18 months intervals. The drought results showed a severe drought at the Karaj station in 2013 and an extreme drought at the Qazvin Station in 2008. The result of investigation of annual dust showed that Karaj and Qazvin stations with regard to 06 and 07 codes had the highest dust phenomenon. Monthly evaluation also showed that the most frequent events occurred at the stations of Karaj and Qazvin in the months of May and June. By examining the relationship between drought and the frequency of days with dust, it was also found that there was no significant correlation between these two phenomena at the Karaj station on an annual and monthly scale, but at Qazvin Station between days with dust and SPI values in the 18-month interval, there was a weak correlation in 10% level, but there was no significant relationship between other time scales.

The phenomenon of climate change is one of the most important environmental challenges that has been increased by the increasing emissions of greenhouse gases and has negative impacts on base resources such as groundwater resources. Therefore, it is essential that research is conducted to understand the impact of climate change on groundwater resources in the future period, to avoid the deterioration of the situation by proper management. Considering the importance of the subject in this study, the climatic model of LARS-WG and the GMS groundwater model were used to study the effects of climate change on groundwater under different scenarios in Kerman plain. In this regard, the climatic emission scenarios of A2, A1B and B1 of HADCM3 model was investigated in the period of 2011-2030 and to study the effect of climate change on groundwater drop, groundwater level was predicted using GMS software for the period 2012-2030 and the effect of climatic scenarios was studied on it. In order to groundwater modeling, the height of water in the piezometers was introduced to the model as a steady state in October 2002 and the values of hydraulic conductivity and recharge and boundary conditions were estimated. Then, the model was run for unsteady conditions for 2002 to 2013, and the groundwater level was predicted under different climatic scenarios for 2014-2030. The results showed that the average of groundwater drop in all four scenarios was -0.86, -5.85 and -9.19 meters (the first scenario), -9.26, -9.36 and -9.49 meters (the second scenario), -12.09, -12.21 and -12.28 meters (the third scenario) and -15.43, 15.55 and -15.58 meters (the fourth scenario) for A2, A1B and B1 scenarios, respectivelyThe phenomenon of climate change is one of the most important environmental challenges that has been increased by the increasing emissions of greenhouse gases and has negative impacts on base resources such as groundwater resources. Therefore, it is essential that research is conducted to understand the impact of climate change on groundwater resources in the future period, to avoid the deterioration of the situation by proper management. Considering the importance of the subject in this study, the climatic model of LARS-WG and the GMS groundwater model were used to study the effects of climate change on groundwater under different scenarios in Kerman plain. In this regard, the climatic emission scenarios of A2, A1B and B1 of HADCM3 model was investigated in the period of 2011-2030 and to study the effect of climate change on groundwater drop, groundwater level was predicted using GMS software for the period 2012-2030 and the effect of climatic scenarios was studied on it. In order to groundwater modeling, the height of water in the piezometers was introduced to the model as a steady state in October 2002 and the values of hydraulic conductivity and recharge and boundary conditions were estimated. Then, the model was run for unsteady conditions for 2002 to 2013, and the groundwater level was predicted under different climatic scenarios for 2014-2030. The results showed that the average of groundwater drop in all four scenarios was -0.86, -5.85 and -9.19 meters (the first scenario), -9.26, -9.36 and -9.49 meters (the second scenario), -12.09, -12.21 and -12.28 meters (the third scenario) and -15.43, 15.55 and -15.58 meters (the fourth scenario) for A2, A1B and B1 scenarios, respectivelyThe phenomenon of climate change is one of the most important environmental challenges that has been increased by the increasing emissions of greenhouse gases and has negative impacts on base resources such as groundwater resources. Therefore, it is essential that research is conducted to understand the impact of climate change on groundwater resources in the future period, to avoid the deterioration of the situation by proper management. Considering the importance of the subject in this study, the climatic model of LARS-WG and the GMS groundwater model were used to study the effects of climate change on groundwater under different scenarios in Kerman plain. In this regard, the climatic emission scenarios of A2, A1B and B1 of HADCM3 model was investigated in the period of 2011-2030 and to study the effect of climate change on groundwater drop, groundwater level was predicted using GMS software for the period 2012-2030 and the effect of climatic scenarios was studied on it. In order to groundwater modeling, the height of water in the piezometers was introduced to the model as a steady state in October 2002 and the values of hydraulic conductivity and recharge and boundary conditions were estimated. Then, the model was run for unsteady conditions for 2002 to 2013, and the groundwater level was predicted under different climatic scenarios for 2014-2030. The results showed that the average of groundwater drop in all four scenarios was -0.86, -5.85 and -9.19 meters (the first scenario), -9.26, -9.36 and -9.49 meters (the second scenario), -12.09, -12.21 and -12.28 meters (the third scenario) and -15.43, 15.55 and -15.58 meters (the fourth scenario) for A2, A1B and B1 scenarios, respectivelyThe phenomenon of climate change is one of the most important environmental challenges that has been increased by the increasing emissions of greenhouse gases and has negative impacts on base resources such as groundwater resources. Therefore, it is essential that research is conducted to understand the impact of climate change on groundwater resources in the future period, to avoid the deterioration of the situation by proper management. Considering the importance of the subject in this study, the climatic model of LARS-WG and the GMS groundwater model were used to study the effects of climate change on groundwater under different scenarios in Kerman plain. In this regard, the climatic emission scenarios of A2, A1B and B1 of HADCM3 model was investigated in the period of 2011-2030 and to study the effect of climate change on groundwater drop, groundwater level was predicted using GMS software for the period 2012-2030 and the effect of climatic scenarios was studied on it. In order to groundwater modeling, the height of water in the piezometers was introduced to the model as a steady state in October 2002 and the values of hydraulic conductivity and recharge and boundary conditions were estimated. Then, the model was run for unsteady conditions for 2002 to 2013, and the groundwater level was predicted under different climatic scenarios for 2014-2030. The results showed that the average of groundwater drop in all four scenarios was -0.86, -5.85 and -9.19 meters (the first scenario), -9.26, -9.36 and -9.49 meters (the second scenario), -12.09, -12.21 and -12.28 meters (the third scenario) and -15.43, 15.55 and -15.58 meters (the fourth scenario) for A2, A1B and B1 scenarios, respectively .

This research was done in order to submit a model for salinity map made with TM satellite data and salinity values in a Buienzahra. The necessary processings such as principal component analysis and producing of different indices was done on the main bands. The 38 soil samples using random sampling (with 10×10 km dimension) from different horizons were designed and performed on the study area. The position of each node was registered with global positioning system (GPS)، and the surface electric conductivity of samples was measured using EC meter instrument in soil saturation extract. Correlation between spectral values (main bands، produced indices) with electrical conductivity values were investigated for 80% of the samples. The regression analysis of ECe showed that there is a significant correlation between ECe with spectral data in all of main bands and with BI، NDMI، SI1، SI2، SI3 indices in 99% levels. The accuracy assessment of estimations using validation 20% samples was done. Results showed the produced ECe model could predict the soil salinity with ME and RMSE of 0. 08 and 2. 53 dS/m respectively. At finally، Salinity map with different salinity classes (0-2، 2-4، 4-16، 16-32، 32< dS m-1) was produced.

Wind regime data from the Jask meteorological station from a 20-year statistical period 1985-2005) was analyzed using the Fryberger method to investigate sand drift potential (DP) in this area. The resultant drift potential and directional variability of wind were calculated and illustrated. Average annual sand DP was estimated to be 460 vector units (VU), and the sand flux is 51 m3/m.year. Therefore, in view of the wind’s erosive power (calculated by software using the Lettau-Lettau equation) and according to Fryberger’s classification 1979), this area is categorized as a high energy wind environment. Calculating DPt in different seasons showed that the strongest winds blow in summer and winter (158.8 and 152.4 VU), and autumn had the lowest DP. In summer, the wind blows in a southsoutheast pattern which differs from that of other seasons. Autumn also has the highest frequency of winds swifter than threshold velocity. The unidirectional index (RDP/DPt) value is 0.08 for this region, which causes the formation of transverse dunes (barkhanoid) from south-southeast to north-northwest.

Sand dunes mobility is one of the serious problems in arid regions. Since wind regime is one of the important factors in sand dunes formation, its frequency, direction and magnitude can be effective. Amount of wind energy and its directional variability (wind regime) have significant control on the morphology and maintenance of aeolian landforms. By recognizing of morphometric and morphodynamic characteristics, it is possible to control soil erosion. Thus, statistical analysis of wind data using Tsoar index (sand dunes mobility index) in the study area was considered to understand wind regime role in volume and direction of transported sediments. Sand drift potential amount of the study area is 100.8 v.u. and sand flux is 2.358 m3/m.year resulted from Lettau – Lettau equation. Considering wind erosion power, the study area is in low class based on Fryberger & Dyne (1979) classification. In spring the wind blowing pattern has west direction and is different from other seasons. Also, the most frequency of blowing wind higher than threshold velocity occurs in this season. Unidirectional index value is 0.64 for this region that results formation of transverse dunes (barkhanoid).