Geography and Environmental Sustainability
Year:2022 | Volume:11 | Issue:41|Papers Count:7

Today, climate change has become a major challenge for human societies. The widespread concern of environmental scientists and researchers about global warming has prompted them to study the time series of climatic parameters, especially temperature to reveal the trends of these parameters over the past few decades. Due to the importance of this issue, the present study was conducted to investigate the trend of spatial changes in the minimum temperature of Iran. In this regard, V20CR networked minimum temperature data with daily time resolution and 1 in 1 degree spatial resolution during the period 2019-1836 have been used. First, the trend of minimum temperature changes was calculated using Mann-Kendall test. Then, using spatial statistical methods such as global and local Moran index and hot spot analysis, the patterns governing Iran's minimum temperature and their dispersion were identified. The results of this study showed that in the cold months of the year the zone is trendless, while the decreasing trend is dominant in Iran in the warm months of the zone. An increasing trend has been observed in the southern regions and to some extent in the southeast of Iran. A declining trend occurred in northwestern Iran in January and is gradually spreading throughout Iran in other months. In May, it reaches its maximum expansion in Iran. According to the World Moran Index, the minimum temperature in Iran has a pleasant pattern. The local Moran index also showed that the northwest of Iran follows the pattern of low cluster and the southern regions of Iran follow the pattern of high cluster. The findings from hot spots indicate that the northwestern regions have cold-temperature spots (negative spatial autocorrelation) and the southern regions have hot-temperature spots (positive spatial autocorrelation).

Today, due to global competition increase, cities are looking for the ways to increase their competitive position among other cities Tourism is a factor in urban competitiveness. Tourism, as the factor that improves urban competitiveness through tourism in national and international markets, caused to facilitate planning for the development of cities. This is an applied which is based on the exploratory factor analysis (Q) method as a mixed or combined method and 50 experts were selected by purposive sampling on the subject of research. In the qualitative stage and based on the summary of the discourse space from 50 propositions, finally 36 propositions were selected for the Q sample. In the qualitative stage after collecting the information obtained from the Qsorting, the Q-factor analysis method was used; it is the main method to analyze the Qdata matrix. They were analyzed by varimax rotation method. SPSS software was used to discover the existing mental patterns identifying the influential variables by calculating factor scores. Using research indicators, 5 different mental models or factors with a total variance of 82. 27% were calculated including political-institutional, economic, socio-cultural, physical and environmental factors. The variables were categorized in order of priority; in political-institutional factors, the variable of macrogovernment policies regarding the region, in economic factors, the appropriateness of the price of tourism services, in socio-cultural factors, increasing the culture of tourism, in physical factors, development of tourism facilities and services and in the environmental factor, climate change had the highest scores in each factor, respectively. Generally, the physical factor has a more specific value than other factors and is more important.

Temperature is one of the critical environmental parameters which is spatially heterogeneous, affecting biological, physical, and social interactions within an urban ecosystem. Therefore, the main purpose of this article is to investigate the contribution of various land covers in forming urban heat islands (UHIs) and urban cool islands (UCIs) and determining the pattern of UHI-UCI in an arid and semiarid urbanization region of Karaj, Iran in July 2020. To achieve the goal, initially, the land surface temperature (LST) was retrieved using mono-window algorithm; maximum likelihood method was applied to generate the land cover/land use (LULC) map using Landsat 8 OLI-TIRS data. Then, the contribution index (CI) of each LULC in creating UHI and UCI was calculated. The thermal environment of the city was evaluated using Urban Thermal Field Variance Index (UTFVI). The results showed that built-up surface (0. 2) and green space (0. 76) contributed the most in creating UCI, while the barren cover played a major role (1. 53) in creating UHI. In addition, the urban hot spots were seen in the industrial zone and bare land (adjacent to the Payam airport) in the southwest of the region. The UTFVI analysis showed that the central areas of the city (old and dense residential areas) were ecologically better as compared to the urban periphery. Therefore, it is necessary to implement mitigation strategies in the marginal areas of the study area. In general, the results of this research can be helpful in urban planning to moderate urban temperature in ecologically stressed zones.

The polar vortex oscillation is one of the prominent manifestations of troposphere and stratosphere interaction, which plays an important role on the climate extremes. Therefore, in the present study, the concept of linking the daily extreme precipitation with the polar vortex and its control effect on synoptic systems was analyzed in the temporal-spatial analysis of the polar vortex at north-west stations of Iran. In this study, according to the statistic-synoptic approach, at first, the homogeneity of precipitation data and precipitation trend of all six stations were examined by linear regression test. 6 components explaining 90% of the data variance were identified in order to analyze the companionship and awareness of the trajectory and the effect of polar vortex on the studied stations, the location of the polar vortex investigated by T-type principal component analysis. Then, the position of vortex in each of these patterns was investigated by considering the polar vertex reagent contour in the geopotential elevation maps of 500 hPa level. 6 general patterns were recognized by analyzing temporal and spatial position of the vertex during the given days in which spatial position, the extension and depth of the ridges were different. The highest positioning of the vertex was seen in the first pattern and the ridges obtained from vertex had the highest depth and expanse on the Black Sea. In all patterns, the daily extreme precipitation was caused by the placement of the receiving vessel from the polar vortex on the required area, which was due to the establishment of the massive Rex and Omega dams on Europe.

This study was conducted to investigate the effect of vegetation in the city in the form of green space on Land Surface Temperature (LST) and also to identify the thermal islands of the Kerman city. LST was calculated by inverse method Planck function using Landsat 8 in Google Earth engine. The calculated LST was calculated as the average of two images in the middle months of the four seasons of 2014, 2017 and 2020. Landsat Science Notebook and a split window were used in order to evaluate the efficiency of the method used in calculating the LST. The relationship and the effect of vegetation on the calculated LST for Kerman city have been done using correlation and selection of ring buffer at intervals of 50, 100, 150 and 200 meters. Finally, their seasonal changes were examined using Moran's I autocorrelation index and its position changes were analyzed both as a season-to-season trend and as a general trend. The results showed that the Planck function method and the Landsat Science Notebook method had more accurate results than the Split window method. There is a relationship between the area of the park and its temperature, and the lowest calculated temperatures for green spaces are related to the parks with the largest area. Correlation test analysis showed that in all seasons of the year, LST is inversely related to vegetation density index. Also, the amount and intensity of this negative correlation vary depending on different seasons. The highest negative correlation value of-0. 48 was recorded for the summer in 2014. Quantifying the effect of green space on the ambient temperature fluctuation showed that, on average distance 200 meters from vegetated areas, the temperature has increased by 3 degrees resulting from increasing distance from the identified cores as green space; it is clear evidence indicating the effect of green space on the amount of measured temperature. The results of this study showed that the calculation of earth surface temperature provides reliable results in the management of urban space that can be useful in future urban decisions.

Precipitation plays a decisive role in meeting the water needs of various crops, dam reserves, feeding surface and groundwater resources, and the occurrence of floods and droughts. Lack of access to long-term daily rainfall data and the high cost of setting up ground meteorological stations necessitate the replacement of low-cost methods with high-precision, high-volume data such as satellite data. The aim of this study was to evaluate the accuracy of precipitation data predicted by TRMM and GPM satellites in Bandar Abbas metropolis during a 17-year period from 2000 to 2017. Hourly precipitation data of TRMM and GPM satellites were obtained from databases. After analyzing the data format in MATLAB environment, hourly precipitation information was extracted. The results showed that the accuracy of both TRMM and GPM models in precipitation forecasting was appropriate and close to each other; it was often overestimated so that 75% of TRMM model precipitation forecasts and all forecasts GPM models were overestimated. The results showed that the TRMM model was more accurate than the GPM model in accurately predicting the occurrence of rainfall events and had less error in predicting unrealistic rainfall and the highest accuracy of the TRMM and GPM models is on a monthly, annual and daily scale, respectively. The value of EF index in TRMM model varies from-284. 52 to 0. 71 and in GPM model from-25514 to-1. 25. The value of the EF index in the TRMM model predictions was positive in 42% of events, while, in the GPM model, it was not positively predicted in any event. The general conclusion of the research is that TRMM satellite is a suitable tool for monitoring and forecasting precipitation

Drought is a natural phenomenon that has a significant impact on agriculture and also influences different aspects of people's lives in both arid and semi-arid regions. Vegetation cover is one of the living components of the ecosystem and plays an important role in many ecosystem processes that are strongly affected by climatic events such as drought. In the present study, the status of vegetation changes in relation to drought index has been investigated in the Ilam catchment (Ilam city). In this research, the 30-year precipitation of synoptic stations of Ilam, Mehran, Dehloran, Sarableh, Ivan, Darrehshahr and Abdanan were applied to calculate the standardized precipitation index (SPI). The Landsat satellite images were used to extract the standard vegetation difference index (NDVI) and also to investigate the detection of vegetation changes in the study area. Besides, the relationship between the SPI drought index and NDVI vegetation index was performed by the Pearson correlation method between raster layers of NDVI and SPI. The results of the SPI index showed that the drought in the years 2003 and 2008 occurred with more intensity than other years. Moreover, the vegetation classification map obtained from the NDVI index showed a decreased trend in the level of vegetation from 1988 to 2018 which is occurred mostly in the dense vegetation category (15959 ha in 1988 and 6492 ha in 2018). Based on the results of the present study, it is concluded that the changes in vegetation over time is directly related to the severity of drought, which should be considered by managers and decision-makers in the natural resources for vegetation management.