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All the water vapor of atmosphere is contained in a column of the atmosphere that is capable of precipitation and it is from the ground to the final of water vapor called perceptible water. This element influenced by topography and height. The purpose of this study is survey about impact of local and spatial factors on distribution of perceptible water maximums in Iran.For this reason, pressure data, especially moisture, orbital and meridional components extracted from NCEP/NCAR and analysis. Correlation and regression methods were used in this study. In order to better survey about perceptible water gradient changes and gradient changes of maximum of perceptible water has been calculated. Results showed that among the spatial factors, height has greatest impact on the spatial distribution of the maximum of perceptible water. Unlike many scientists who believe that by increasing the latitude perceptible water reduced, this rule is less In Iran atmosphere. However, most of the gradient changes of perceptible water occurred in some parts of the Zagros highlands, West and South West. The results of cycle analysis showed that the maximums of perceptible water in Iran have short term cycles between 2 to 4 years.

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Understanding weather zoning and knowing homogeneous climate regions are essential for land use and regional planning. The aim of this study was to compare three different geographical climate of Iran, the Caspian coastal, mountainous and arid interior of the provinces of Mazandaran, Semnan, Tehran, Qazvin, Qom, and Markazi. In order to do this study, climatic data of 56 synoptic and climatology stations and 19 climatic elements were used by using cluster analysis and factor analysis models. For this purpose, a matrix with dimensions of 56 x 19 and the R configuration and as a database was used for segmentation. By applying factor analysis based on principal components analysis with Varimax orthogonal rotation it was determined that in the climate of these three regions, four factors of humidity, temperature, dust and thunderstorms are affecting more than 85% of the total variance of the climate of this region. The hierarchical cluster analysis method and integration into the matrix of factor scores, four main and several sub-region areas were identified.The main areas are hot, dry desert area, ​​low rainfall mountain slopes, the mountainous and cold and semi-rainy regions and high rainfall and finally the moderate high rainfall. The study of four areas and their local and regional conditions shows that the neighborhood with humidity source such as the Caspian Sea and rough configurations such Alborz Mountains play a decisive role in the formation of north sub-areas.

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Nowadays most of the countries have chosen the tourism as a development strategy for the rural areas. The Hajij village with worthy natural, cultural and anthropological heritage has known as a tourism destination in Kermanshah province and west of Iran. In this research, the effects of tourism are studied in this village. The methodology is descriptive-analytic method. The necessary data were collected through both library and field methods. The population of this study equals to 180 persons- based on Cochran Table- including the 130 residents, 15 local governors and 35 visitors were selected by simple random method. Research tools include a researcher made questionnaire which its validity was confirmed based on the collective opinion of the universities professor's and experts of executive organizations. The reliability of the questionnaire was calculated at 0.867 using Cranach’s alpha test based on 20 pretesting. Data analysis was held by factor analysis in SPSS 21 software. The research findings showed that there are six main factors among 30 variables which interpret 70 percent of the variance of the tourism effects the most important is physical- facilitator effects. Moreover, the positive effects were %65.8 and the negative ones were %34.2 based on factor analysis.

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The aim of this study is synoptic analysis of heavy precipitations in Kurdistan Province during 1/1/1961 to 31/12/2010(18263days). By using two thresholds of extensively and intensity of precipitation, 107 days have been recognized. In temporal view, the selected days occurrences in wet seasons of year that start from October to June. By applying the principle component analysis on the sea level pressure during the 107 days, the results showed that 12 components explain about 93 percent of sea level pressure variation. The results of the applying cluster analysis on the component score showed that two circulation patterns result in occurrence of extreme and heavy precipitations in Kurdistan province. In order to recognize heavy precipitation in dry season (June to October), the area mean precipitation for the province have been calculated. According to the 99 percentile threshold and extensively of precipitation the heavy phenomena recognized. By two thresholds 32 days selected and same as to the wet season precipitation the sea level pressure data from NOAA extracted. The data analyzed by Principle Component Analysis and 11 components extracted that explain the 93 percent of variation sea level pressure in the frame study. By doing cluster analysis on the 11 components, one pattern recognized for the sea level pressure.  The results showed that in lower atmospheric levels (1000, 925 and 850 hpa) occurred highest convergence on the study region during wet season. The source of humidity in the lower levels is Arabian, Red, Oman, Persian Gulf and Mediterranean Seas. In the high level atmosphere, the value of convergence is low. The sources of humidity in high levels atmosphere are Red and Mediterranean Seas. In the mid level atmosphere (500 hpa) the presence of trough results in nutrition of the instability on the study region. Also during dry season, the highest moisture flux convergence occurred in the low levels but the resource of the moisture is Caspian Sea. In the upper levels (from 700 hpa to up) in addation to Caspian Sea, Mediterranean and Red sea are the resource of heavy precipitations.

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Sustainable development is an environmental concept appropriate to our era that nowadays in all of economic, social, environmental and physical-‌spatial aspects is considered and focused by everyone. In these times, global organizations that are working around the issues of sustainable development, emphasis on rural sustainable development, which seeks to improve rural living standards and welfare of the inhabitants of the villages, because at present time, the procedure of socio–economic variations accompanied by increasing migration of human groups is led to evacuation of villages. With attention to the importance of the subject and the increasing instability of the villages, present study was done for determining the effective factors on rural instability and measuring the intensity of this instability in Kaki District of Dashti Township. The research method is descriptive - analytical in which whole inhabited villages of Kaki District of Dashti Township were surveyed. In this regard, according to the number of households living in villages and using the Cochran formula, 255 questionnaires were calculated for questioning andthese questionnaires have been completed in the villages in proportion to the population of each village. In these questionnaires, 34 indicators related to the four dimensions of sustainable development (environmental, social, economic and physical-spatial) are considered. In order to determining instability intensity of the villages and their spatial analysis, is used AHP method in Expert Choice and ArcGIS software. Also SPSS software is used for statistical analysis. The results show that at the present time, all villages have various degrees of instability. In this regard, 65.8 percent of these villages have severe or very severe instability.  

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Trees can record long-term effects of climate variables. Using dendroclimatology knowledge, we can reconstruct such variables especially for areas which have short-term climatic data. For this purpose, we reconstructed the temperature degree of the warm months (May-September) through annual rings width of Quercus persica and regression analysis of data obtained from stations on Dena region. With this goal in mind, three growth heights were selected in Dena Forests and 52 growth samples from 26 bases were extracted and their growth rings width were measured with LINTAB5 with an accuracy of 0.01 mm. After cross dating stage, to eliminate non-climate effects, May to September temperature average and tree rings time series were standardized. The Residual Chronology (RES) calculated by ARSTAN was calibrated with temperature degree of the period 1882-2011 and its positive and significant correlation with the width of growth rings was confirmed. Based on the relations between the calculated chronology and joint statistical temperature degree data, the reconstruction of temperature degree of the warm seasons for over a century was performed and it was found that in the last three decades, region's average temperature of May to September had an increase in comparison to the average of the previous century.  

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Attention to housing and planning in line with sustainable development in the framework for planning national and regional is to our use of the knowledge and techniques of programming, existing situation and issues related to it is made clear that. To the main goal this research study and the analysis of the most tiresome effective in planning housing classification level and province of the country based on.This is a descriptive method of research-analysis. collecting data with the use of the results of the census and public housing in 1390 and resources library and documents and evidence have been done. The index of the study, 27 indicators inclusive of housing is a little bit you returned home Models based on regional planning(Multi-criteria decision analysis Vikor) Has beenanalyzed. Using a factor analysis technique, These four factors were  total 91/18 % of the variance explained Calculation of productivity. The provinces have indices using vikor. Based on this classification level, Esfahan(Zero coefficient),Golestan(Zero), Tehran(zero), East Azerbaijan(0/079 coefficient) in first to fourth levels are very Brkhordar And Semnan provinces(0/87),­­Ardabil(0/88), Sistan and Baluchistan(0/90)          ,Bushehr(0/90), Lorestan(0/91), Hormozgan(0/91),Mahal­­   Bakhtiari (0/92), Kohkiluyeh &Bourahmd(0/94),Qom(0/94),North Khorasan (0/96) and Elam(1), Respectively, and thelastlevelsvery brkhordar havetheleast.