correlation

 Investigation of the relationship between droughts and the number of days associated with dust in East Azerbaijan Province. 

 First, using the correlation equation, the missing value of the number of days with dust of the synoptic stations was completed in the last 30 years. Then, the trend of this data was checked using the Mann-Kendall test. The frequency and severity of droughts were calculated using the SPEI method in the R program. The impact of droughts on the occurrence of dust was evaluated through the Spearman correlation test.

 The presence of a trend in the data series at a significant level of 0.05 has been confirmed, and Sen’s slope values also indicate that this trend is increasing. The results of the SPEI index show that drought periods are expanding, especially in the second half of the selected period in the region. On the other hand, there was a significant correlation at the 0.01 level between the number of days with dust and the SPEI-6 drought index.

 The playa of Urmia Lake is the main focus of local dust generation, and this fact is confirmed based on the correlation between the dust and the SPEI index and the observation of suspended particles with lake origin in the air of the stations near this lake.

This research aims to reveal the correlation between the occurrence of dust phenomena in connection with the continuation of droughts and the expansion of the playa of Lake Urmia, which has calculated the SPEI index based on the latest statistics of meteorological stations. Conducting such research is necessary to evaluate the tempo-spatial distribution of wind erosion, identify areas at risk of desertification, and formulate control strategies for these areas.

The continuation and stability of the river catchments depends on the passage of water and the transfer of sediment. Just as river flow information is vital for many practical applications such as water allocation, long-term planning, catchment management operations, flood forecasting, design of hydraulic structures, etc., amount of sediment yield in rivers is important from a variety of aspects such as river morphology, engineering designs to river water resource, reservoir dams, river organization to Inhibition of erosion and floods, irrigation networks, etc. are important. Proper and sustainable use of water and soil resources within the river catchments requires awareness of the spatial variations of hydrogeomorphic elements (water discharge, sediment discharge) and the determinants of these variations. Various studies refer to the key role of forests in the water cycle, soil protection and habitat protection, and show that increasing or decreasing forest cover has had an effective role in the hydrogeomorphic regime of the rivers. Due to the importance of interdisciplinary studies and understanding of interactions between different biological and non-biological components of river catchments in regards to sustainability and continuity of natural and human environment, the present study intends to be aware of the quality and quantity of relationships between forest cover and discharge/sediment yield in the Talesh catchments, NW. Iran.

This study was based on correlation and regression analysis of the ecohydrological relationships. The research statistical population included the Talesh region and the research sample population included 12 important catchments in this region. The data used included monthly water discharge and sediment yield data of hydrometric stations on the one hand and the digital satellite images included the Aster digital elevation model (DEM) and Landsat 8 image on the other hand. Data analysis tools include geographical information system (GIS), Google Earth, SPSS and Excel. The steps of conducting the research were such that: firstly, the hydrogeomorphic variables including the mean, maximum and minimum rates of water discharge and sediment yield (S.Y), were calculated for 2020yr. Then, the 12 watersheds were extracted based on the position of the hydrometric stations from the DEM with a spatial resolution of 30 meters. The next step was to extract the forest cover from the Landsat 8 image, which was obtained on June 3, 2022. In this regard, the satellite image classification in the form of two class of forest and non- forest was through the maximum likelihood algorithm. Satellite images were classified using 300 training points. Finally, the correlation test between the forest cover and water discharge and sediment yield was performed in the SPSS software. The significant level of correlation relationships was ≤0.05.

The results of the correlation test showed that there was a significant relationship between the percentage of forest cover and the water discharge in the catchments. The correlation coefficients (R) were -0.58, 0.58, -0.46, respectively for mean, maximum and minimum discharges, indicating that definite and positive role of the forest in preventing rapid and erosive runoff. In contrast, the relationship between the percentage of forest cover and S.Y in the studied catchments was not significant and the correlation coefficients for mean, maximum and minimum S.Y are at -0.07, -0.05, -0.06 respectively, which is indicative of the weak correlational relationship between the two variables. However, the separation of catchments as large catchments and small led to improvement of correlation relationship between forest cover and S.Y, despite being insignificant, so that correlation coefficients in small basins for mean, maximum and minimal S.Y, respectively of -0.656, -0.60, 0.339, respectively. Given the significant relationships, it was possible to provide regression prediction equations for monthly mean and maximum discharge based on the percentage forest cover in the catchments. The insignificant relationships between forest cover and the sediment load can be explained by complex nature of the sediment load transfer, especially storage and re-movement of sediment load, making it difficult to establish significant relationships between the sediment yield (S.Y) and the environmental variables affecting it. On the other hand, the relationship between vegetation and the S.Y in catchments is usually complex and nonlinear, and therefore the use of nonlinear relationships may make the relationship more complete and obvious. Finally, the multiplication of the factors involved in the production and transferring of sediment load makes it difficult to model and predict the spatial variations of sediment load.

The present study attempted to provide a quantitative analysis of the spatial relationship between the forest cover and the rates of water discharge and sediment yield (S.Y) in the Talesh catchments by adopting an interdisciplinary approach. The results of correlation analysis show that the presence of significant and inverse relationships between the percentage of forest cover and water discharge of the catchments reflects the distinct role of the forest in reducing runoff and the incidence of dangerous floods in the Talesh catchments. In contrast, the relationships between forest cover and S.Y in the studied catchments are not significant. This conclusion shows that we are not able to provide a prediction model of S.Y variations based on the percentage of forest cover in the catchments, but the reverse relationship between the percentage of forest cover and the S.Y of the catchments implies the protective role of the forest in adjusting the process of precipitation, runoff- erosion and enhancing of the environmental quality in catchment systems. First of all, it should be noted that the forest cover variable is considered as a modulator variable or interface between the inlets and outputs of the catchments system, so it is likely that the direct role of the forest on runoff and sedimentation couldn’t be easily grasped by statistics analysis. In this context, there are various natural and human factors that may play a more effective role in the S.Y variation of the catchments than the factor of forest cover. Increasing of correlation coefficients (R) between the percentage of forest cover and S.Y in small catchments indicate that these catchments are an ideal scale for identifying controlling factors of sediment yield.

The atmosphere is a fluid and interconnected system. Any change by known and unknown factors in a limited part of the atmosphere can cause larger changes in the general circulation of the atmosphere with the passage of time, this itself causes a change in the water share of each region of the water cycle.The researches that have been carried out in order to predict rainfall in different areas of Iran have mostly focused on atmospheric indicators, which were initially made to predict rainfall or study climate changes in areas other than Iran. In fact, in these researches, it shows the effect of special atmospheric circulation patterns, such as Walker, North Atlantic Oscillation, on rainfall of Iran. On the other hand, pressure changes in limited areas have been analyzed to find a suitable predictor (index) for seasonal rainfall of Iran. It is necessary to carry out complete and comprehensive research (sea level pressure patterns on a planetary scale) to investigate the relationship between sea level pressure changes and precipitation in different climatic regions of Iran.

The study area is considered for research and investigation of the explanation of the changes in the time series of precipitation in the annual time scales by the spatial changes of the sea level pressure in the time lags of one to six months, in the northeastern region of Iran (Khorasan Razavi, Northern Khorasan, and Southern Khorasan provinces).Precipitation data for the 35-year statistical period (1987-2021) on a daily basis for the selected synoptic stations including of Mashhad, Sarkhs, Qouchan, Torbat Haidarieh, Sabzevar, Kashmer, Gonabad, Birjand, Nehbandan, Tabas, Ferdous, Qain, Beshravieh and Bojnoord was obtained from the Iranian Meteorological Organization.In order to investigate the relationship between changes in sea level pressure and annual rainfall in northeastern Iran, monthly data of sea level pressure were used. These data are presented and updated on the website of the National Center for Atmospheric and Oceanographic Studies affiliated with the United States of America since 1979. In general, regression and correlation methods are used to determine the presence or absence of a relationship between variables. The correlation index shows the relationship between two variables that are both affected by common factors. Both of these variables are random and other unknown factors jointly affect their occurrence. Monthly time series of sea level pressure data for the time period from 1987 to 2021 were made for 10512 cells (2.5 by 2.5 degrees of arc). The months of September, August, July, June, May and April respectively correspond to the delays are one to six months. In order to be able to investigate the quantitative and spatial changes of precipitation with sea level pressure in the six months before the onset of precipitation, correlation maps were drawn for each of the six months (time delay of one to six months). In the second step, the significance of the correlation was investigated using inferential statistics (hypothesis testing) and the points whose correlation was significant on the map was drawn In the third step, for the practical use of sea level pressure changes to predict precipitation, the points whose correlation was significant (places with maximum correlation) were selected as predictors (independent variable). Using the data of these selected points and multivariable regression, rainfall forecasting models were fitted. The performance of these models in rainfall forecasting was evaluated using indices such as correlation coefficient, explanatory coefficient and root mean square errors. Models with the best performance in predicting rainfall with a delay of one to six months were selected. The places where the prediction models had the best performance in different months (September, August, July, June, May and April) using the data of those regions were introduced as key regions in the prediction.

The results obtained from the correlation study show that there is a significant correlation between the patterns of changes in sea level pressure and precipitation (12 months from October to September of the following year) with a delay of one to six months. In fitting precipitation models based on sea level pressure changes using selected points from the centers with maximum correlation, the obtained results show that the pressure changes in the eastern and western Pacific Ocean, especially in theeastern ocean, in all annual rainfall forecasting models as a predictor has been selected. Changes in sea level pressure in the Pacific Ocean seem to play an important role in predicting annual precipitation.According to the independent variables, Southeast Asia and regions from the Southeast China Sea and Guam Island to northern Australia can be other important indicator regions in predicting one-year rainfall. In examining the performance of forecasting models in delays of one to six months, it was found that fitting models with a delay of 5 and 6 months have better performance than other models. So that the 5 and 6 month delay models explain 70% and 65% of the rainfall changes, respectively.

The findings of this research show that it is possible to obtain favorable results from changes in sea level pressure patterns in the centers with maximum correlation for rainfall forecasting (12 months from October to September of the following year). Since sea level pressure data are available from various sources, it makes us somewhat independent from the data of physical models of atmospheric circulation. It also provides the possibility that we can have a proper prediction of the amount of precipitation for a year a few months before the beginning of rainfall. Since there is no 12-month rainfall forecast for one-year water resource management policies in the agriculture, industry, drinking water, natural resources and environment sectors, and on the other hand, due to the large range of annual rainfall changes in the northeast Using average rainfall cannot be useful and effective, the results of this research can be helpful.

Soil and air are two essential elements in the life of creatures on the earth. Their interaction in certain conditions can cause many risks; among these dangers, dust storms can be mentioned. Under the conditions of dust storms, a large amount of dust is emitted in the air and the horizontal visibility is reduced to less than 1000 meters. Therefore, due to the important role of the dust phenomenon, it is highly necessary to understand the spatial-temporal changes and to analyze their long-term variations. Jazmurian region, located in the southeast of Iran, between the two provinces of Kerman and Sistan and Baluchistan, has become completely dry and turned into a desert due to drought and construction of numerous dams, and has turned this region into one of the key areas of dust production in the country. Therefore, the purpose of this study is to investigate the temporal-spatial changes of dust storms in relation to climatic parameters in Jazmurian wetland, which is of particular importance in order to properly manage this area to face the problems caused by dust storms.

In order to carry out the present research, the meteorological data related to the synoptic stations located in the Jazmurian wetland area for a period of 20 years (2000-2020) were received from the Iranian Meteorological Organization. Climatic data used in this research include temperature, precipitation, relative humidity, wind speed and direction, horizontal visibility, and remote sensing data including Aerosol Optical Depth (AOD) and Normalized Difference Vegetation Index (NDVI). The research method uses a combination of statistical analysis, observation and remote sensing. In order to check the AOD and NDVI, the monthly data of MODIS sensor was used. In this study, 12 synoptic stations that had the longest and most complete statistical periods were used in order to identify the temporal-spatial changes of dust occurrence in Jazmurian wetlat. The Mann-Kendall test was used to examine the trend of time changes. The slope of the Sen estimator was used to check and confirm the accuracy of the trend changes. In order to better understand the spatial distribution pattern of dust events in the Jazmurian wetland basin, the inverse distance interpolation method (IDW) was used. Also, Pearson's spatial correlation analysis was used to investigate the mutual effects between the indicators.

The 20-year average review of the indicators showed that the maximum value of aerosol optical depth in the studied area was 0.3, which is seen in the central part of the wetland. The vegetation index also showed that the maximum value of this index was 0.2, which covers most of the northern, northwestern and western parts. Examining the average rainfall trend showed that the maximum and minimum rainfall in this period are about 219 and 85 mm, respectively. Meanwhile, the 20-year average temperature survey showed that the maximum and minimum temperatures were 28.7 and 17.2 degrees Celsius, respectively. The fact that the maximum rainfall is in the northern and western part is in harmony with the minimum temperatures in these areas. The examination of the average wind speed showed that the maximum speed was 3.6 m/s, which was mostly in the central, west and northwest, south and southwest parts. The results of the wind direction index showed that the lowest wind direction is in the northern parts of the region and the highest wind direction is in other parts of the Jazmurian wetland basin. The maximum humidity in the studied area is 43.73%, which is mostly seen in the north, northwest, west, south, and southwest parts.  Examination of the horizontal visibility index showed that the maximum amount of horizontal visibility was in the northeastern and northwestern parts of the region. Also, the process of changes in the Mann-Kendall test showed that the annual averages of the indicators of wind direction, wind speed and horizontal visibility have been increasing with a positive slope, and the trend of changes in the relative humidity index has a negative slope and indicates a decrease in the period of 20 years. The results of the correlation analysis (at a significance level of 5%) showed that the highest correlation of the optical depth index was with the wind direction parameter and the lowest correlation was with the relative humidity index.

The growing trend of the emission of dust particles caused by the phenomenon of wind erosion in recent decades has caused major concerns at different regional, national and global levels. Therefore, it is necessary to understand the temporal-spatial changes of the dust caused by these events in order to reduce their adverse consequences in different regions. Accordingly, in this research, with the help of this knowledge, information related to the optical depth of particles in the air (AOD) and the vegetation cover index (NDVI) were investigated using the MODIS sensor satellite data. Using the meteorological data received from the website of the National Meteorological Organization, the climatic data of rainfall, temperature, relative humidity, horizontal visibility, wind direction and wind speed were evaluated in the period of 2000-2020 and finally the correlation between the AOD index and other climate parameters was evaluated. The results of the optical depth investigation showed that the maximum optical depth is located in the central parts of Jazmurian wetland. The trend of changes in rainfall and temperature indicators also showed that the trend of these two parameters was increasing; this increase in temperature is more noticeable. Correlation investigation between optical depth and other parameters showed that the highest correlation of optical depth index was with the wind direction parameter and the lowest correlation was with the relative humidity index. In general, we can conclude that the optical depth of airborne particles is highly dependent on environmental factors, which is more evident in arid and semi-arid areas, especially in Iran and the Jazmurian wetland area. In recent years, the area of the wetland has acted as a source of dust. Therefore, by using the remote sensing data obtained from the MODIS sensor and the climate data, it is possible to examine and analyze the trend of dust changes in the units of time and space.

Glacier cirques show the characteristics of past glaciers and climates. In this research, the analysis of 39 glacial cirques in the catchment area of Jajrud River was done. For this purpose, Arc GIS software and Google Earth images were used. The parameters of length, width, height of the top of the circus, height of the floor of the cirques , area, perimeter, ratio of length to width, ratio of length to height of floor and ratio of width to height of floor were used to check the morphometry of cirques. For each of the morphometric parameters, statistical factors of coefficient of variation, standard deviation, average, maximum and minimum were calculated and estimated in Excel. Then R2 values or coefficient of determination were estimated for each of the parameters and a scatter diagram was drawn. Finally, the correlation matrix was estimated using the Pearson correlation coefficient for all factors. The highest abundance of cirques is located in the southwest direction. The maximum height of the cirques is 3800 meters and belongs to the geographical direction of the south.The highest correlations between length and width parameters were observed at the rate of 0.9936. The results show that the cirques in the north-facing slopes have a lower height. This indicates the high nutrition of these cirques and their significant volume in the Pleistocene period. Investigations showed that more developed cirques have more area, less height and less length to width ratio than less developed cirques .

Due to the Earthchr('39')s climate is systemic, the Large-scale atmospheric patterns, are considered as one of the most important factors that affects the climate in different regions of the world. This issue indicates necessity of investigating these patterns and their effects on climate areas beyond a given region. In this study, the relationship between North Hemispherechr('39')s teleconnection indices and Evapotranspiration (ET) at synoptic stations in south of the Aras River basin during the 26-years statistical period (1992-2017) was investigated.  Run test was used to evaluate the sequence and homogeneity of data. Pearson correlation coefficient was used to assess the relationship status between these patterns and the changes in the studied variable. The highest significant correlation coefficient calculated between ET stations and some patterns was obtained with non-delayed mode, with 1, 2 and 3-months delay which are weak in terms of intensity.While in different, the highest significant correlation coefficients was observed that it was Calculated at Ahar station (in winter, spring and autumn seasons with EA, SCA, NAO and EA indices, respectively, amount 0.79*, -0.53**, -0.49** and -0.43*), at Ardabil (in spring with SCA, in summer and autumn with POL (-0.54**, 0.44* and -0.56**) and at Parsabad (in autumn with EP/NP 0.46*). Significant correlation between phases of EA, EA/WR and EP/NP, SCA positive phase and PNA negative phase with Z-Scores of some stationchr('39')s was observed low to moderate intensity at the confidence level of 0.95 and above. The results  of the backward regression model, in addition to introducing the most effective  indices in the modeling of stationchr('39')s ET, showed that  they do not have the explain this variable in Aras basin.

Desertification manifests itself in its initial stages with less production in the ecosystem. In this situation, the balance of water and nutrients for the growth of plants becomes unfavorable compared to the past. In natural conditions, the ecosystem of dry areas is in a stable state in terms of water and energy conversion, but due to human intervention, this balance is subject to change. Obviously, when the vegetation is destroyed, the land becomes bare, its organic matter is destroyed and the structure of the soil is destroyed. In this situation, direct rain on the soil causes further destruction of the soil structure. The natural consequence of such conditions is the lack of rainwater storage and the drop in the level of underground water tables. Usually, severe reduction of vegetation, water and wind erosion, salinity, soil compaction, sedimentation and emptying of nutrients are considered as the main signs of desertification. According to the definition of the Convention to Combat Desertification (UNCED), desertification is the destruction of land in dry, semi-arid and dry and semi-humid areas due to human and climatic factors, which as a result reduces the productive power of the land. Water and soil are among the most valuable natural resources, and human societies, both farmers and ranchers, are active and dynamic agents of changing natural landscapes, and improper management, neglect, and excessive exploitation can lead to soil destruction, and as a result, human life is seriously threatened. be placed.

To achieve the goals of the research; Modis sensor images were obtained from the NASA database during the years 2000-2020. In order to calculate the land surface temperature (LST) from the MOD11A1 product, to extract the Normalized Difference Vegetation Index (NDVI), from the MOD13Q1 product, to extract the Net Primary Production (NPP) index, from the MOD17A2H product with a spatial resolution of 500 meters in a time frame of 8 The MOD43A1 product of the Terra satellite was used to extract the albedo index. After preparing the MODIS sensor images, the indices of land surface temperature, normalized difference of vegetation cover, net primary production and albedo were estimated from the images and their correlation was checked.

Based on the results, LST is shown in three classes: low, medium and high. So, low to south moderate in the northwest and west and higher temperatures in the east of the study prevail. And based on the results, NDVI is classified into four areas with very poor, poor, moderate and dense coverage. Thus, the very weak layer is located in the north and northeast side, the weak layer is located in the northwest to south side, and the medium and dense layer is located in the northwest to southwest side. with increasing temperature; The normalized difference index of vegetation decreases and there is an inverse relationship between LST index and NDVI. with increasing temperature; Primary net production decreases and there is an inverse relationship between LST index and NPP. Based on the obtained results, it can be acknowledged that there is an inverse relationship between LST index and Albedo.

Based on the results obtained from the fourth chapter; The LST index during 2000-2020 was divided into three classes of low temperature, medium temperature and high temperature. So that; The largest area of the studied area had an average temperature, which was observed in the south and southwest. In the eastern and northeastern regions, the temperature was higher than in other regions, which corresponds to Namak Lake and Qom. Also, areas with low temperature were covered only in small parts from the northwest to the southwest of the region, which corresponds to the uneven heights of Karks. The NDVI index during 2000-2020 was divided into four classes: very weak, weak, medium and dense. So that; The largest area of the studied area had weak to weak silt cover, which was observed in the northwest to east range, which corresponds to Namak Lake and Qom. Also, it covered areas with medium to dense coverage only in small parts from the northwest to the southwest of the region, which corresponds to the uneven heights of Karks. Based on the results of NPP index changes during 2000-2020, the highest amount of primary net production was observed in the northwest to southeast of the studied area. This shows that the amount of primary net production is higher in this range. Also, the minimum amount of primary net production in the range from northwest to south corresponds to the heights of Vulture to Tanam, which indicates low production. Based on the results of changes in the Albedo index during the years 2000-2020, the highest amount of albedo was observed in the northeast to the east, so that these areas correspond to the salt lake and areas with weak vegetation, low net primary production and high temperature, because albedo and surface temperature The earth is affected by the amount of vegetation and relatively small changes in the characteristics of the vegetation cause changes in the albedo and temperature of the earth's surface. Between the normalized difference index data of vegetation and land surface temperature, net primary production and land surface temperature and albedo and land surface temperature, there was a significant relationship at the one percent level so that the correlation between vegetation and temperature, net primary production and temperature and albedo and the temperature was reversed and equal to 0.80, -0.84 and -0.85 respectively. The results of the study made it possible to create a desertification program by presenting a practical program for the Syr Regressive Road Dam and took an effective step towards sustainable development.

Teleconnection is one of the features of the climate on a global scale. Teleconnection patterns represent large changes that occur in the pattern of atmospheric waves and tornadoes and affect the pattern of temperature, precipitation, the direction of showers and the position and intensity of tornadoes in large areas.   

The aim of this study was to investigate the effect of these patterns on temperature series in Zahedan.

In this regard, minimum temperature, maximum and average temperature statistics of Zahedan station during the period of 1987-2019 on a monthly scale as well as standardized data of teleconnection patterns during the mentioned period, were used. Pearson correlation and multivariate regression tests were used in this regard.

Geographical Context: 

The geographical territory studied in this research is the city of Zahedan.

The results indicate that there is a significant relationship between teleconnection patterns and the average temperature of Zahedan. Among these, NTA, AMO and TNA patterns had the greatest effect on the average temperature of Zahedan. The correlations were direct, and only the NAO pattern was inversely correlated. The maximum and minimum temperatures of Zahedan also showed the highest correlation with the patterns located in the North Atlas. The maximum temperatures in March and October and the minimum temperatures in March and August had the highest correlation with the North Atlantic patterns.  

In general, it can be said that the patterns located in the North Atlas, more than other patterns, have affected the temperature series of Zahedan and especially its average temperature.

Because of rapid urbanization, urban warming has become a serious problem along with global warming. In this study, the impacts of urbanization on minimum and maximum temperature variations were investigated in Iran's Northwestern urban areas. For this purpose, the meteorological (1987-2017) and urbanization data from the general census as well as the housing population (1365-1395) in conjunction with Pearson Correlation-coefficient and Mann-Kendall Test were used. Based on this test, a positive and significant trend was observed at minimum and maximum temperatures among urban stations. The analysis of decade's changes also shows the impact of urbanization on climate. The results of the relationship between minimum and maximum temperature with the number of population in urban areas are positive.The correlation between the minimum temperature in Khalkhal and Khoramdareh (small towns) was 0.8, in Maragheh and Khoy (average cities) 0.7 and Maximum temperature was 0.61 in Mahabad city and there was a significant relationship between the big cities of Ardebil and Zanjan At 0.7, 0.75, but in Urmia and Tabriz (a very large and metropolitan city), the population with a minimal temperature was relatively small, but maximum temperatures in both cities with a coefficient of 0.68, 0.61, indicating the significant role played by the urbanization on the urban climate. In small and medium-sized towns, significant changes were observed at minimal temperatures. At all stations, the demographic changes and the minimum and maximum temperature increases were observed. Major changes in the decades of 1375-1385 and 1385-1395, in which population growth is increasing.

The purpose of this study is to investigate the variables that form the courtyard system of Kashan houses (from a morphological point of view), to improve the climatic comfort of the house in Kashan and to determine the significant of correlation between them. These variables include length, width, height, courtyard elongation, garden pit, pond area, garden area, number of courtyard, number and location of the Sardab and Houzkhane and the orientation of the courtyard. The study area of the old urban fabric of Kashan, which is surrounded by Jalali fort and the statistical population is houses in the old part of city. The type of applied research and descriptive research method is using statistics, analysis based on SPSS outputs and content analysis. In this study, twenty houses were selected as a sample from the statistical population and each of the research variables was reviewed based on field visits and written sources and checked for Pearson correlation and summarized by content analysis and then concluded. This study is innovative in terms of number and type of variables and examining the correlation between variables for the courtyard of Kashan houses. The results show that the central courtyards of Kashan have an average area of 440.6 m2, length 33.8 m, width 17.5 m, body depth 6.75 m, central courtyard elongation 1.38, pond area 48.7 m2 The area of the garden is 86.6 m2, the percentage of the area of the pond from the yard is 10.98, the percentage of the area of the garden from the yard is 19.7, the average number of yards is 1.55, the average number of Sardaab is 1.75, 50% have a pond and 70% have a garden pit and 65% have a southeast orientation. have a .  The variables of width and length and yard area and pond area and garden area are correlated with each other and yards with an area of more than 500 square meters have two Sardab.

Earth's surface temperature is an important factor in global warming studies, and today it is the main challenge for many researchers worldwide. With remote sensing technology, it is possible to evaluate the temperature of the earth's surface and land use changes during different years with the help of satellite images, thermal infrared radiation, and physical models. In environmental studies, due to the location and location of the observations in the sample space, traditional statistics cannot be used due to the continuous structure of time and space. For this purpose, spatial statistics (spatial autocorrelation) is a suitable and new method for analyzing these data.

In this research, satellite data related to Landsat 5 and 8 images for the years 1990 and 2020 were obtained from the American Geological Survey. After correcting the images, the land use maps of Jiroft city were prepared and using the combination of visible and infrared bands, land use maps were prepared. The transformation of different land use classes and their changes during these years were analyzed in IDRISI software. Also, 150 control points from Google Earth were exploited to evaluate the accuracy of classified maps. In order to obtain the temperature of the earth's surface, the thermal bands of the received Landsat images were used. In two steps, the spectral radiance was converted to the temperature of the black body, and the surface temperature of the earth's surface was calculated. Finally, to reveal the spatial pattern of local differences, the local Moran's spatial autocorrelation statistic has been exerted.

The results showed that from 1990 to 2020, part of barren and flood channel lands was converted into water areas, which increased with the dam's construction after 1990. The average temperature of the earth's surface increased by 11.1 degrees in 30 years, which can be seen in all uses. The reason for this increase can be seen as the increase in air temperature. Another reason for the increase in the temperature of the earth's surface is the increase in construction in the region. The classification of the earth's surface temperature classes showed that the very hot and warm classes in the southern parts increased in 2020, and the average (most change) and cold classes decreased. The local spatial correlation statistics analysis results showed that hot clusters are gradually concentrated in southern regions and cool clusters in northern and northeastern regions.

The findings of this research showed that despite the increase in agricultural and garden use and the decrease in barren areas, the earth's surface temperature would increase to a large extent in all uses. Although manufactured areas are also increasing, the main reason for the temperature of the earth's surface can be considered the increase in air temperature and climate change.

Temperature changes are one of the most important issues of human life in recent years and it is known as the most important manifestation of climate change in the current century. The current study was conducted with the aim of investigating the minimum temperature changes in the eastern basins of Iran and its relationship with the sea surface temperature teleconnections patterns. In this regard, the monthly minimum temperature data of Mashhad, Sarakhs, Birjand, Zabol, Zahedan and Saravan stations and the data of 14 teleconnections patterns of sea surface temperature changes during the statistical period of 1987-2019 were used. At first, minimum temperature changes were investigated using new methods of spatial statistics and hot spot analysis. Then, the relationship between the studied variables was checked using Pearson's correlation test and linear regression. The results show that in all months, hot spots were observed in the southern and eastern areas of the basin and cold spots were observed in the northern and western areas of the basin. Hot and cold spots were observed in May more than other months and hot spots covered 31.87% of the study area and cold spots covered 32.58%. Correlation of the studied parameters also showed that in terms of time, the patterns of distant connection with the minimum temperature of January showed more correlation than other months. In contrast, there was no correlation in the months of June and November.

Network construction is an acceptable approach for better understanding the behavior of complex system which can be used to reveal the pattern of collective dynamics for realizing physical interactions in the dynamical system. In this case, characterizing functional connectivity of complex networks for studying a broad class of natural and artificial systems from the measures of correlation and causality is of utmost importance to correctly unravel physical phenomena of the system. Many network reconstruction approaches are based on heuristically thresholding the correlation matrices resulting from pairwise correlation analysis according to experimental methods. Other approaches compare the observed correlations against null models in the statistical analyses, obtaining results which are statistically robust. Different methods were used, including cross-correlation (CC), spectral coherence (SpeCoh), mutual information (MI), transfer entropy (TE), Spearman's rank correlation (SC) and convergent cross-mapping (CCM). The methods were applied to linear and nonlinear collective dynamics by autoregressive moving average (ARMA) and Logistic map (LOG) models, respectively. The dynamics of interconnected units was simulated from different complex topologies widely observed in empirical systems with well-known network models. The methods of MI and CCM were chosen after examining on the artificial cases consisting of desirable features of the real systems. The results show that network reconstruction dramatically depends on the interplay between the (unknown) underlying structure and the (unknown) underlying dynamics and high values of the area under curve of receiver operating characteristic around 72% obtain for the selected network topologies.

Sustainability is one of the new approaches defined in the system of urban studies that is fully in line with sustainable development. Indeed, the biodiversity approach focuses on the overall health of an urban system. Therefore, the purpose of this study is to analyze and explain the correlation pattern between dimensions and indicators of viability in Zahedan. The qualitative and quantitative methods as well as applied research, software such as LISREL, SPSS, and factor analysis were used to analyze the obtained data. The results of the Kayser-Meier and Walkin (KMO) criterion showed that with a significance level of less than 0.05, factor analysis is significant to identify the structure and factor model. The results of LISREL software show a good proportion of data biodiversity dimensions. The second-order factor analysis also showed, that all factor loadings are meaningful and support the indicators and components well. The results of the study showed a correlation matrix of viability, the viability of the highest correlation between the size of the urban environment and urban infrastructure facilities, and a correlation coefficient of 0.81. It can also be concluded that the correlation coefficient between the components is of a high significance level of 0.000.

The main purpose of this study is to investigate changes in atmospheric thickness and its relationship with temperature changes during the cold period of the year. To achieve this goal, data on geopotential elevation and temperature of 1000 hPa for the years 1960 to 2020 have been extracted from the NCEP NCAR site. In the next step, using the programming capabilities of Gardes, numerical extraction of atmospheric thickness between the levels of 1000 to 5000 hPa per meter and temperature data of the level of 1000 hPa in degrees Celsius was performed. Four 15-year periods are divided. The spatial distribution of the 15-year average and the 60-year long-term average, as well as the positive anomalies showed that the thickness of the atmosphere increased significantly during the first to the fourth period, and this increase reached its maximum in the fourth period. The study of the trend in the first period has shown a decrease in the thickness of the atmosphere for the whole of Iran, but with the beginning of 1976 (beginning of the second period) we have witnessed a sharp increase in the thickness of the atmosphere, which continued until the fourth period. The results of Pearson correlation coefficient showed that there is a significant relationship between changes in atmospheric thickness and temperature changes in the cold period of Iran at the 95% confidence level. Spatially, this correlation reaches its highest level in the west, northwest and southwest of the country. As a result, it can be said that the thickness of Iran's atmosphere has increased significantly in recent years, and one of the reasons is the change in the nature of inflows to Iran and, of course, global warming.

The concept of morphometry involves the measurement and numerical analysis of land surface, shape, dimensions and form of land. In relation to flooding, watershed morphometry includes quantitative indicators describing watershed physical characteristics that control the pattern and quantitative characteristics of floods such as amount, time of occurrence, delay time, and flow hydrograph. Unusual development of cities due to population growth and consequent land use change has caused disruption of hydrological balance and increased flooding of basins. The purpose of this study was to prioritize the sub-catchments of Zab River based on a novel combination of morphometric analysis and statistical correlation and zoning of flood potential under sub-catchments.

to disregard for human and social sub-basins, which has a great impact on the hydrological processes of the catchment. Based on the validation results, the Shinabad and Sufian basins have the highest priority, and the new method of morphometric analysis and statistical correlation have considered them the highest priority for managers' attention.The elongation ratio helps to understand the hydrological characteristics of the drainage basin and ranges from 1 for circular basins to 0 for extended basins and its high values ​​indicate the shape of the basin circle, high peak discharge and high flood potential. Is. The values ​​of tensile strength in all sub basins are more than 0.5 and indicate high potential for flooding. Straller (1964) considers the circle ratio a quantitative measure for visualizing the shape of the basin. High values ​​of this parameter indicate circular shape, high to medium ruggedness and low permeability in the basin, which causes peak discharge in less time. The ratio of the circular ratios under the Lavin Tea Basins is 0.17, Copar 0.19, and Zab Small 0.27, indicating relatively low flooding potential in this parameter. The amount of this parameter is 0.35 under Shinabad basin and 0.31 under Sufian basin which indicates their higher flood potential in this parameter (Table 7). The branching ratio is an important parameter affecting peak runoff hydrograph discharge with high values ​​indicating high instantaneous discharge and flood event. The mountainous and steep areas have a split ratio of 3 to 4. Branching ratio values ​​indicate low flooding potential except for Shinabad basin which shows this ratio of 6.19 and this ratio indicates high flood potential
 

Since hydrological units are based on morphometric parameters to prioritize flood mapping, firstly, using channel networks and elevation curves, topographic maps of 1: 50000 and digital elevation data are analyzed. The boundaries of the hydrological units became. Then, because the catchment morphological parameters have different effects on soil erosion processes and runoff formation, prioritization of the sub-basins was done in a new way based on the difference of morphological parameters and statistical correlation analysis between them. Morphometric parameters were calculated for all sub-basins in GIS software. Then, statistical correlation of morphometric parameters was performed based on the t-Kendall method using SPSS software. Based on the correlation matrix we can analyze the relationship between the parameters and define the relative weight for each parameter without All twelve variables including circular elongation ratio, branching ratio, flow frequency, drainage density, drainage texture, compaction index, shape factor, mean slope, roughness ratio, roughness, and roughness number for all sub-basins are calculated in Table 2. The Kendall correlation coefficient was used to investigate the relationship between Validation results showed that the new method of morphometric analysis and statistical correlation did not perform well in prioritizing all sub-basins, but this method was accurate in identifying the most priority sub-basins (most acute conditions). The reason for this may be due to the lower performance of morphometric analysis and the characterization of waterways in low-slope catchments. Also, the precise reason for not estimating the priority of some sub-basins is due.

The selected twelve parameters are directly related to runoff and flood potential. Therefore, high values ​​of parameters have a direct relationship with runoff and flood potential due to their greater impact on the selected twelve parameters. Indicator (Cv) values ​​for each sub-basin are obtained from averaging of 12 indices and accordingly sub-basins of Shinabad and Sufis with high flood potential, sub-basin with small potential and sub-basin zab basin. Copar and Lavin Tea have physiographic and morphometric characteristics of the sub-catchments have a great influence on flooding and hydrological behavior, it is possible to study the status and potential of flooding below the catchments. In this research, in order to potential of flooding, firstly, Zab sub-basins were prioritized based on the new method of morphometric analysis and statistical correlation. Based on this method, the results showed that the Shin abad and Sufi basins are the top priority for the implementation of management measures to ensure.

Drought is one of the environmental disasters that is very frequent in arid and semi-arid regions of the country. Rainfall defects have different effects on groundwater, soil moisture, and river flow. Meteorological drought indices are calculated directly from meteorological data such as rainfall and will not be useful in monitoring drought if the data are missing. Therefore remote sensing technique can be a useful tool in drought measurement. Drought is a recognized environmental disaster and has social, economic, and environmental impacts. Shortage of rainfall in a region for long periods of time is known as drought. Drought and rainfall are affecting water and agricultural resources in each region.

The present study is a descriptive-analytic one with emphasis on quantitative methods due to the nature of the problem and the subject under study. In this study, the Tera Sensor Modis satellite images from 2000 and 2017 were used to verify the existence of wet and drought phenomena. In the next step, by examining the rain gauge and synoptic data of the existing stations and using a standardized precipitation index model of three months (May, June and April), the sample was selected. Next, we compared the temperature status indices (TCIs) and vegetation health indices (VHIs) in these three months to determine the differences in these indices over the three months. Modi satellite Tera satellite was used to find out the vegetation status in the study area. Subsequently, using the condition set for the NDVI layer, the vegetation-free areas were separated from the vegetated areas. Experimental method was used to determine the threshold value of this index. For this purpose, different thresholds were tested, with the optimum value of 1 being positive. NDVI is less than 1 plant-free positive and more than vegetation-free. MODIS spectral sensing images for ground surface temperature variables, with a spatial resolution of 1 km, including bands 31 (bandwidth 1080/1180 central bandwidth / 11.017 spatial resolution 1000 m) and 32 bands- 770/11 Central Wavelength Band 032/12 Spatial Resolution Power (1000 m) Selected for months that are almost cloudless. All images have been downloaded from the SearchEarthData site and have been edited. The total rainfall of June, April, and May for the 20-year period was provided by the Meteorological Organization of Iran. ARC GIS software and geostatistical methods were used to process the Excel data. Also, to estimate the correlation between the data Pearson's correlation coefficient was used.

The standardized precipitation index is a powerful tool in analyzing rainfall data. The purpose of this study was to compare the relationship between remote sensing indices and meteorological drought indices and determine the efficiency of remote sensing indices in drought monitoring. Correlation between variables with SPI index was evaluated and calculated. The results of the indicators are different, so a criterion should be used to evaluate the performance of these indicators. SPI index on quarterly time scale (correlated with vegetation) as the preferred criterion Selected. According to the results of correlations, the TCI index with the SPI index had a strong correlation with other indices. In the short run, this index has the highest correlation with thermal indices at 1% level. The correlation between meteorological drought index and plant water content and thermal indices increases with increasing time interval. Positive correlation between vegetation indices and plant water content with meteorological drought indices indicates that trend of changes is in line. Therefore, the TCI index makes drought more accurate and is a better method for estimating drought.

The results showed that among the surveyed fishes, the highest drought trend was observed in the eastern part of these provinces and covered more than 50% of the area. The trend of changes in this slope was statistically significant. According to the results of correlations, the TCI index with the SPI index had a strong correlation with other indices. It can also be concluded that the Modis images and the processed indices along with the climate indices have the potential for drought monitoring. Using maps derived from drought indices can help improve drought management programs and play a significant role in mitigating drought effects.

Predicting and obtaining information about stream flows is vital for many practical applications such as water allocation, long-term planning, catchment management operation, flood forecasting, optimization of hydropower production, designing hydraulic structures, and so on. On these accounts, experts have always attempted to accurately estimate river flows and provide contributions to the existing methods. It is possible to establish a relationship between discharges of catchments based on sufficient statistics and their geometry characteristics by using regional analysis and multivariate regression in a relatively homogeneous region in order to estimate discharges of the catchments without statistics or with insufficient statistics in that region. Owing to the need for a better modeling of water discharges in terms of seasonal variations, the present study tried to regionally estimate the monthly mean, minimum, and maximum discharges in Kashfarud Watershed based on geomorphometric variables in order to: (1) examine the possibility of scientific and useful generalization of surface water quantity in the whole watershed and (2) identify and determine important geomorphometric factors influencing the water discharge variations of Kashfarud sub-watersheds. Kashfarud as a vital flow in this region, including Mashhad plain, has played an important role in supplying water to the inhabitants of Mashhad metropolis and other surrounding cities such as Chenaran and Torqabeh. Besides, extensive engineering facilities, such as Golestan, Torogh, Kardeh, and Ardak dams, have been built on the tributaries of this river. 

The research was grounded upon statistical analysis, including correlation and regression tests. For this study, we made use of monthly average (low, medium, high) discharge data and resolution of 30-m DEM. The discharge data were measured in 10-gauge stations of 10 sub-watersheds over 20 year (1997-2017). In this regard, we considered the monthly discharge values ​​of 10 stations located in 10 sub-basins during the water years of 1997-2016. The research proceeded as follows: after selecting the common statistical period, we calculated the monthly average flows recorded in the stations over 20 years. Next, 2 months with the highest and lowest flows were selected in each station. Also, we considered the values ​​of the two selected months besides the average monthly discharges in the stations (average of 12 months) as the dependent variables. Afterwards, we embarked upon to calculate the independent geomorphometric variables (16 variables), which were determined by using DEM in the GIS environment. The independent variables included perimeter, area, minimum elevation, average elevation, maximum elevation, elevation range, average slope, maximum slope, main stream slope,  main stream length, total stream length, drainage density, Schumm’s roughness index, Gravilius coefficient, elongation, and concentration time. After calculating the values ​​of the 3 dependent and 16 independent variables for each station, we tested them based on a two-way correlation to find out which variables had a significant relationship by means of the correlation matrix. The significance level of the correlation relationships was ≤0.05. Finally, we  presented an estimate model of the dependent variables via the independent variables. This was done based on the dependent and independent variables that had significant correlation relationships.  

The results of the correlation analysis showed no significant relationships between the minimum monthly discharges and the independent variables.  This was probably due to the dryness condition and small variance in the low monthly discharge rates among the sub-watersheds. In contrast, significant relationships were obtained among the monthly mean and maximum discharges and 6 geomorphometric variables including the average elevation, maximum elevation, elevation range, average slope, drainage density, and roughness ratio. The correlation coefficients of all significant relationships were above 0.6, indicating a close and strong relationship between these variables. The direct correlation of altitude, slope, and roughness with monthly mean and maximum discharges indicated that on the one hand, the monthly discharges of the sub-watersheds were strongly dependent on the altitude variables dominating the other environmental factors while on the other hand, the roughness intensity and active dynamics associated with the variables of altitude played an important role in water flowing and transporting from upstream to downstream of the catchments so that the speed of hydrological responses of the catchments increased by the rise of roughness severity. In contrast, the negative correlation between the drainage density and the dependent variables was unexpected and probably associated with hydroclimatic conditions indicating characteristics of the dry and geologic conditions, permeability of some formations, and presence of joints and fissures in the rocks. The predictive regression models of the monthly mean and maximum discharges had good accuracies and efficiencies and could explain 80 and 90% of variances of the monthly average and maximum discharges, respectively. 

Modeling the spatial variations of discharges in watersheds requires consideration of hydrological limit values both daily and seasonally, along with normal and average values. By adopting such an approach, the present study aimed at modeling monthly water discharges in Kashfarud sub-watersheds based on geomorphometric variables. The multivariate regression analysis among the independent variables including the average and maximum monthly flows and 16 independent geomorphometric variables revealed that the regression models could be obtained through the variables of average elevation, maximum elevation, elevation range, average slope, drainage density, and roughness ratio. These factors could explain the major parts of variance of the independent variables. It is possible to generalize the monthly mean and maximum discharges achieved in this study to other sub-watersheds through the resulting models with respect to the low estimation errors and high accuracies. However, generalization the monthly minimum discharges is not possible due to the lack of a significant correlation between this variable and geomorphometric variables. It is necessary to estimate the monthly minimum discharges in Kashfarud Watershed by reconstructing and converting the data or extending other predictive models. Reliable estimations of low water discharges provide us with information about water supply for the environment and water quality management for sustainability of healthy ecosystems. Also, owing to the role of small mountain catchments in the occurrence of peak flows, prioritization of watershed management measures in such catchments is incumbent to reduce the risk of floods. 

One of the Factors affecting on the trend of changes in dust phenomenon, is the fluctuations of climatic parameters and consequently drought. The purpose of this study was to investigate the trend of changes in Dust Index (DSI) and its relationship with drought indices (SPI and SPEI) in the southern range of Alborz. After of receiving the meteorological codes, seasonally-annually ten years DSI was calculated and zoned. Then standardized monthly DSI, SPI and SPEI stations with thirty-year period and assessment DSI temporal changes trends, using of Man Kendall and Petit statistical methods and finally, its correlation with SPI and SPEI was performed by Pearson method. The results showed that from west to east, increased the intensity of the dust and in spring was observed with maximum intensity. Annual changes of The DSI (2017-2008) are irregular and do not have a trend, as in 2008, the intensity of dust high and the next year sensible a decline, similar to 2016 and 2017 seen. Also the trend of DSI time changes showed that the west and central stations had a significant trend and as we move eastward the temporal changes were irregular and without significant trend. On the other hand, there was no significant correlation between DSI and SPI indices and there was a negative relationship between DSI and SPEI except for the western part of the region which in some stations is high-intensity, this significant relationship, and with standardization, this relationship becomes even more evident. In general, considering precipitation with temperature and evapotranspiration has shown a better correlation with the dust index, as well as changes in socioeconomic factors as an influential factor on dust should be discussed and examined.

The purpose of this study was to investigate the relationship between climate change and skin cancer in Ardabil province. Multiple regression equations were used to analyze the relationship between climatic variables (frost, sunshine hours, mean humidity, maximum absolute temperature, minimum absolute temperature and average temperature) with changes in skin cancer at the stations. To determine the independent variables affecting skin cancer changes in the studied stations, a backward regression model was used. The results of this study showed that the correlation between climate variables and skin cancer in the studied stations is relatively significant and these correlations are often strong and significant at 0.01level with each other. The direct correlation between skin cancer and other climatic variables in the studied stations showed that the incidence of skin cancer in the warmer half of the year was more than the cold half of the year. Quantitative correlation coefficient and percentage of changes explained by backward model to justify the number of skin cancer incidence in the studied stations under the conditions of entry of all climatic variables and also in the case of removal of some low climate variables (entry of high-impact climatic variables ) showed that in the studied stations, the entry of all climatic variables in determination the amount of skin cancer incidence, between 79 and 91, and the withdrawal of single or multiple variables, and the remaining variables in backward regression model, range from 76 to 89 percent of these changes are explained.