Journal of Spatial Analysis

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Understanding the climate of a region as a first step and most immediate action is considered research for development projects Climatic phenomena such as floods every year irreparable damage to the soil, pastures, forests, urban and rural facilities, human and animal import Climatic phenomena such as floods every year irreparable damage to the soil, pastures, forests, urban and rural facilities, human and animal import. The first factor in causing flood is rainfall intensity that occurs at a certain time. Therefore necessary infrastructure projects, and one of the main issues in hydrological and hydro-climate is awareness of the occurrence and amount of rainfall, most likely for different periods.

     In order to implement the model of Synoptic convergent in this research and estimated probable maximum precipitation in the South West region of the Caspian

   1: The 1:50,000-scale Digital Mapping the location of all stations in the study area, Climatology, rainfall and hydrometric surveys in selected were identified on the map.

   2: The maximum instantaneous discharge rate of the highest daily rainfall stations selected surveys (1976-2011) are also studied.

   3: collection of the highest daily rainfall statistics selected stations, monthly and annual precipitation data for the period (1986-200),Facts about the daily atmospheric phenomena (cloud, wind speed, dew point temperature, air pressure) with an interval of 3 hours to 3 hours, Statistics continuing 12-hour maximum dew point of the surface (in degrees Celsius) and wind speed times (NAT) for the stations of Anzali, Rasht, Astara, Ramsar, Ardabil, Pars Abad For the first 10-day period, 10-day and 10-day return period for calculating the 50-year-old third, 80-year and 100-year and monthly statistics on the average pressure of the selected stations establishment station.

   4: Select the desired storm rainfall in 24 hours and 48 hours to obtain a return period of 50 years, 80-year and 100-year 12-hour maximum dew point and wind speed persistence for long periods, the separation of each month, and the resolution of each decade, through software SMADA and HYFA.

   5: Purvay of Rain maps and DAD chart is also the main stages of this work.

   6: Finally, weather maps, humidity maps and omega air maps at ground level, 700 level and 850 hp prepared from

    Days prior to completion until the day of rain showers in the stormy period from the NCEP / NCAR site and was ready in GRADS software environment.

     In order to realize the adiabatic saturation warmest period of the most intense storms in 1355-1390The maximum instantaneous rate of discharge and daily rainfall statistics, the most comprehensive and stations on their occurrence in the previous chapter, was studied.So the four pervasive  hurricane was  selected. Then, rain storms map were plotted in the GIS software environment and use of IDW method and Using data from the windy days selected on rainfall stations in the study area. In order to obtain the rainfall in the whole region,were regressed  between the two parameters: precipitation and elevation; and was estimated average of rainfall in the cumulative area and rainfall amount in during of the storm days. Based on the height - area tables of ​​each storm separately, DAD curves was drawn based on average rainfall in columns cumulative and cumulative area. Then we reviewed and interpreted weather maps at ground level, elevation Maps, humidity maps and omega maps at 850 hPa level. Survey maps showed Tongue of immigrant anticyclons in North West Europe that usually is deployed on the Black Sea will advection cold air from the above widths on the Caspian sea and is transmited very wet weather to the south and West south Caspian Sea. After analyzing weather maps, the next step is obtain  to water for showers.To calculate the rain water the best way is getting the hottest adiabatic saturation that occurs with the maximize the dew point temperature and wind speed. After obtaining the maximum dew point and wind speed factor, we would like to calculate the coeffcient storm. After obtaining the coefficients of the storm,obtained its P.M.P by multiplying the amount of rainfall for each storm.

     According to the obtained PMP,was adopted rainfall continued for 24 hours with the numbers 276/95. PMP obtained showed that the storm dated 2/10/2001 of 24-hour duration, has been most intense and pervasive from the two other samples.

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Due to the growth of industries and factories, deforestation and other environmental degradation as well as greenhouse gases have been increasing on the Earth's surface in recent decades. This increase disturbs the climate of the Earth and is called climate change. An Increase in greenhouse gases in the future could exacerbate the climate change phenomenon and have several negative consequences on different systems, including water resources, agriculture, environment, health and industry. On the other hand to evaluate the destructive effects of climate change on different systems, it is necessary to initially study the area affected by climate change phenomena. One of the most important effects of climate change on water resource is Drought.  On the other hand, one of the most serious consequences of climate change is how it will affect droughts and water resources.

Drought along with warmer temperature and less precipitation will threaten the water supplies for the crop irrigation, which will directly reduce the production of crops.The climate of the 21st century will very likely be quite different from the climate we observed in the past. The changes will continue to be large in the future period with increasing carbon dioxide emissions. Analyzing and quantifying the signal of climate change will be much in demand considering the above sectors, which are highly relating to the sustainability and human living.

In the past several decades, global climate models have been used to estimate future projections of precipitation [Intergovernmental Panel on Climate Change (IPCC), 2007], and have led to future estimation of drought, to quantify the impact of climate change and comparing the duration  and intensity of droughts under future climate conditions with current climate by using Atmospheric-Ocean General Circulation Models AOGCMs to predict future Precipitation. Global circulation models namely, coupled Atmosphere-Ocean Global Climate Models (AOGCMs) are current state of the art in climate change research. in This study aims at investigating the impact of climate change on droughts conditions in Iran using the Standard Precipitation Index (SPI).

The precipitation time series have been used for the estimation of Standardized Precipitation Index

(SPI) for three timescales, 3, 12 and 24 months, for the region. The outputs of HadCM3-A2 and A1B were applied for the assessment of climate change impact on droughts. One of the major problems in using the output of AOGCMs , is their low degree of resolution compared to the study area so to make them appropriate for use, downscaling methods are required. In this study we have used lars WG for downscaling monthly average of rainfall of AOGCM-HadCM3, and The HadCM3 outputs were downscaled statistically to the study area for a future period 2011-2040.then, was evaluated by the coefficient of determination (R2) between observed and downscaled data.  A method has been used for the estimation of annual cumulative drought severity-time scale-frequency curves. According to the rainfall results, in the 2011- 2040 period rainfall would decrease  to compared to baseline period in the study area.

The SPI time series were estimated (2011-2040) and compared with the respective time series of the historical period 1961-1990. Results revealed that there are decreases in the frequency of severe and mild droughts for the three examined SPI time series while there are increases in the duration of moderate droughts. This implies that droughts will be a concern in the future during the growing season (for the dominant crop) which should be considered in water resources management. specially in the west station of Iran.

Also, these frequency ratios were mapped by GIS on study area. Results showed that generally in the future periods, frequency of droughts ratio of three months drought time- scale will be increase in the North, North West and some parts of the south Alborz mountains and, The Ratio of long ( 24 months) drought for scenario A2 compare to the current climate shows increasing drought in the parts of the North khorasn, sistan and baluchestan and kerman provinces and parts of South West of Iran. scenario A1B shows increasing drought in the parts of the East of Mazandaran , Tehran , Horozgan and parts of Fars and Yazd  provinces.

Finally ,further  more analysis of drought, AWCDS-Timescale-Return Periods computed. These curves integrate the drought severity and frequency for various types of drought. The AWCDS time series were estimated

for basic period and 2011-2040 under scenarios A2 and A1B. The comparison indicated the three types of drought intensity increases for the three examined SPI time series in the South East of Iran.

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Drought is the most important natural disaster, due to its widespread and comprehensive short and long term consequences. Several meteorological drought indices have been offered to determine the features. These indices are generally calculated based on one or more climatic elements. Due to ease of calculation and use of available precipitation data, SPI index usually was calculated for any desired time scale and it’s known as one of the most appropriate indices for drought analysis, especially analysis of location. In connection time changes, most studies were largely based on an analysis of trends and changes in environment but today special attention is to the variability and spatial autocorrelation. In this study we tried to analyze drought zones in the North West of Iran, using the approach spatial analysis functions of spatial statistics and detecting spatial autocorrelation relationship, due to repeated droughts in North West of Iran and the involvement of this area in the natural disaster.

In this study, the study area is North West of Iran which includes the provinces of Ardebil, West Azerbaijan and East Azerbaijan. In this study, the 20-year average total monthly precipitation data (1995-2014) was used for 23 stations in the North West of Iran. In this study, to study SPI drought index, the annual precipitation data of considered stations were used. According to the statistical gaps in some studied meteorological stations, first considered statistics were completed. The correlation between the stations and linear regression model were used to reconstruct the statistical errors. Stations annual precipitation data for each month, were entered into Excel file for the under consideration separately and then these files were entered into Minitab software environment and the correlation between them was obtained to rebuild the statistical gaps. Using SPI values drought and wet period’s region were identified and zoning drought was done using ordinary kriging interpolation method with a variogram Gaussian model with the lowest RMS error. Using appropriate variogram, cells with dimensions of 5_×5km were extended to perform spatial analysis on the study area. With the establishment of spatial data in ARC GIS10.3 environment, Geostatistic Analyze redundant was used to Interpolation analysis Space and Global Moran's autocorrelation in GIS software and GeoDa was used to reveal the spatial relationships of variables.

The results showed that most studied stations are relatively well wet and this shows the accuracy of the results of the SPI index. Validation results of the various models revealed that Ordinary Kriging interpolation method with a variogram Gaussian model best explains the spatial distribution of drought in North West of Iran. So, using the above method the stations data interpolation related to SPI index in North West of Iran was done. The results showed that Moran index values for the analysis of results of standardized precipitation index (SPI) in all studied years, is more than 0.95. Since Moran’s obtained values are positive close to 1, it can be concluded that drought, in the North West of Iran during the statistical period has high spatial autocorrelation cluster pattern of 90, 95 and 99 percent. Results also showed that in all the years of study, Moran's global index is more than 0.95 percent. This type of distributed data suggests that spatial distribution patterns of drought in North West of Iran changes in multiple scales and distances from one distance to another and from scale to another and this result shows special space differences in different distances and scales in this region of the country. Results also showed that drought in North West of Iran in 2008 is composed of two parts: Moderate drought in parts of West and North West region (stations of Maku, Khoy, Salmas, Urmia, naghadeh, Mahabad and Piranshahr) and severe drought in the southeastern part of the study area (stations: Sarab, Khalkhal, Takab, Tabriz and Mianeh). So the pattern of cluster drought in the North West of Iran in 2008 is on the first and fourth quarter. The results of this index showed that drought and rain periods are similar in the studied stations. The results of the application of Moran's index about identifying spatial distribution of drought patterns showed that The values of the different years during the period,  have a positive a positive coefficient close to 1 (Moran's I> 0.959344) and this shows that the spatial distribution of drought is clustered. The results of the standard score Z values and the P-Value proved the clustering of spatial distribution of drought.

The results of the analysis of G public value, In order to ensure the existence of areas with clusters of high and low values showed that The stations of Maku, Khoy, Salmas, Urmia, naghadeh, Mahabad, Piranshahr and Parsabad follow the moderate drought pattern in the region and are significant at the 0.99 level. Jolfa station also has a mild drought of 0.95 percent confidence level and for Sardasht station is significant in 0.90 percent. High drought pattern in Sarab, Khalkhal, Takab, Tabriz and Mianeh stations was significant in 0.99 percent level and also for Ardabil, Sahand and Maragheh stations very high drought pattern was significant in 0.95 percent level and for Meshkinshahr and Ahar high drought pattern is significant in 0.90 percent. By detection of clusters of drought and rain in the North West of Iran using Moran’s spatial analysis technique and G general statistics a full recognition of the drought affected areas in this region can be obtained and take the necessary measures in its management 

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Drought is a natural occurrence that occurs repeatedly or alternately and is likely to occur in almost every kind of climatic event. Also, the distinction between this phenomenon and other natural disasters is that unlike other disasters, this phenomenon gradually over a relatively long period of time to act and its effects may be delayed after a few years and more than any other natural disaster appears. Several indicators have been presented to decide the characteristics of hydrological and meteorological drought. These indicators are generally based on one or more climatic elements. The SPI and SWI indicators are similar in terms of ease in calculations and results, and use monthly precipitation data and monthly spatial data rates. The simultaneous effect of meteorological droughts on groundwater levels rarely happens. Therefore, the present study investigates the effect of meteorological droughts on the groundwater level of Marand plain and calculates the time delay of drought on groundwater level.
The study area in this study is Marand Plain in East Azarbaijan Province. In this research, we used meteorological data (average monthly rainfall) of 7 rain gauge stations during the statistical period (1980-2012), and the monthly water level data of 23 piezometric wells during the statistical period (2001-2011). The correlation between stations and piezometric wells and linear regression method was used to reconstruct the statistical defects, then SPI and SWI indices were used to study the rainfall and groundwater changes process and the analysis of drought conditions in the meteorological and underground watersheds. The SPI index is basically calculated for periods of 3, 6, 9, 12, 18, 24, and 48 months. Also, the standardized water level indicator (SWI) has been used as a criterion for assessing occur drought and wet years in the Marand plain. The purpose of the SWI index is to allow zoning of groundwater level fluctuations at the study area. Extraction of drought and wet year intensities in different scales and basin zonation for drought maps in Marand plain was first calculated by entering the monthly values in DIP software, SPI values for 12-month time series. SWI values were calculated from monthly data of piezometric stationary level surfaces, such as SPI values, with the help of DIP, Minitab and Excel software. Geostatistical Analyst was also used to decide the weather drought and groundwater drought periods for the ArcGIS software.
 
 
The results of the SPI values showed that meteorological drought is not of a definite local place, while groundwater droughts have not occurred randomly in the area and its concentration in the west of the aquifer is more than the east. Considering the increase in the area under cultivation, to compensate for the water needs of agricultural lands, an increase in the harvesting of underground water table has occurred in order to compensate for the need for water, indicating a tangible relationship between the rainfall and the level fluctuation in the Marand plain. Therefore, considering the increase in the area under cultivation during the years of drought in the region, the best correlation between them was -0.720 with a delay of 5 months, in order to investigate the effects of drought on the surface of the station, which was significant at 1% level It illustrates the impact of groundwater resources with a 5-month delay. Also, the results of the survey of monthly data of Marand plain surface during the statistical period (2001-2011) showed that the groundwater level of the plain had a negative trend that fell by about 2 meters.

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. Caspian Sea south coast future climate change estimations through regional climate model
many physical of the procedures related to climate change are not perceived thoroughly. Scientific knowledge used to show those procedures completely, and to analyses forecasts is so complex, since most current studies about climate physical model have been done through semi experimental and random models and most of the current analysis techniques are still going through early stages. One of the important aspects of this study is modeling physical procedures of sea level rise geographical pattern, which is used practically for SLR threat evaluation of special geographical location, meaning Caspian basin. Since Caspian basin is a closed sea, it is heavily influenced by climate change and meanwhile is changing due to physical level and environmental change. It is necessary to define Caspian coast climate change possibility with specific focus on climatology and meteorology fine data, also to define the scale of sea level fluctuations for the sake of exact planning in different fields. This study aims at presenting a new dynamic method, via using an integrated model system named SIMCLIM, which can clarify SLR satellite changes well.
According to scientific examination existing in this study, based on scatter scenario 4.5 RCP and 8.5 RCP for the following years, until 2100, temperature and precipitation change proposal have been presented. On one hand, Caspian coastal climate change analysis and estimation were based on climate patterns and water flows in the form of regional climate statistical model in order to simulate and forecast, on the other hand surveying chronological changes of Caspian sea coast slope with satellite height measurement was done to measure sea surface height fluctuations The present study has used SIMCLIM model for the first time in order to clarify Caspian sea level changes, elements, and effective climate reasons, all simultaneously in one project. The project base is according to coastal systems and procedures. Coast line shore change simulations are based in Bruun law.
In future the frequency and intensity of extreme events temperature and precipitation will increase. Extreme events illustrate changes in extreme temperature and precipitation measures, in comparison with the base period of 1981-2010 which convey precipitation sum or the temperature beyond 95 percentile of base period. Temperature and precipitation coefficient of variation for the whole Caspian basin is positive and it varies from 25 to 88 percent. A disordered pattern is dominating south basin of the sea. Sea level changes, considering vertical earth movements, which is 2 mm in a year, resulted from subsidence of Caspian pit seabed have been obtained for both scenarios. In general, annual sea level average while ignoring seasonal changes, is increasing consistently and it was calculated 1.22 cm each year according to high estimation procedure in scenario 8.5 RCP and it was 0.93 cm based on scenario 4.5 RCP. Predicted results were compared with real results of base20-year period from 1995-2015. Base period results in three levels of sensitivity of low, mid, high shows 8.4, 10.1, and 11.8 cm rise; after comparing them with model forecast results, meaningful coordination at the level of 95 percent was found out. In both scenarios, all over the Caspian shoreline water advance and destruction will exist. In the worst case scenario of 8.5 RCP of 2030, current coast will decrease about 23 meters and in 2060 it will be about 53 and in 2100, there will be 117 meters advance towards land.
Precipitation and temperature percent for 2030, 2060, 2100 will change increasingly. Spatial variability and annul coefficient of variation are various in different regions. 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 all convey chronological period precipitation distribution with disordered accumulation and more local difference in this region in comparison with other regions. Then Caucasus mountainous region will have the highest increase in precipitation with a suitable scatteredness, during a year. The southern part of Caspian Sea will be with the highest increase in temperature and the least amount of increase in precipitation in percent. High coefficient of variation in this area illustrates abnormal and disordered pattern on the threshold of precipitation for both scenarios.
 fluctuations in sea level based on subsidence of Caspian pit seabed was calculated.In general, average annual sea level is increasing which will be 1.22 cm, per year for scenario RCP 8.5 and 0.93 cm for scenario 4.5. Due to incapability of world community in decreasing releasing greenhouse gases, it is expected scenario that 8.5 RCP to come to reality.
 Caspian Sea shoreline is influenced by water advance and destruction. The difference between two scenarios in 2060 will be 3 meters and in 2100 will be 12 meters. Instinctually, such advances in coasts with less depth and less slope will be more. This study suggests that coastal changes are inevitable. However, this region inhabitant owns no systems or no systems have not yet developed to aid them be able to adopt with the climate changes.
 
Keywords: Sea level rise, South Caspian basin, Extreme event, Coefficient of variations, shoreline.

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evapotranspiration is one of the most important components in water balance and management. In this research, to evaluate the effects of climate change on the amount of potential evapotranspiration in the southern part of the Aras River Basin using the downscaled data of the GFDL-ESM2M model in the CORDEX dynamic downscale under the RCP8.5 scenario during the period of 2021-2050 and its comparison. It is treated with the values ​​of the base period (1985-2005). Data with a horizontal resolution of 22 x 22 km from the GFDL-ESM2M model were used in this research. The findings of the research showed that the minimum and maximum temperature and, accordingly, the ETp of the future period will increase compared to the base period in all six studied stations of Aras Basin (Ardebil, Ahar, Jolfa, Khoi, Mako and Pars-Abad). The value of this minimum temperature increase is estimated between 1.4 and 3.8 ºC and for the maximum temperature between 1.7 and 2.2ºC. The range of annual ETp increase varies from 133 mm to 189 mm. In the monthly ETp scale of all stations from January to July with an increase between 3.9 and 1.64 mm and from August to December with a decrease of 0.7 to 38.2 mm. Estimating the increase of ETp in the future period in the basin, especially in the months of spring, which is very important in terms of water demand, requires special attention to the possibility of this estimated increase in the planning of the water and energy sector.