نشریه جغرافیا و مخاطرات محیطی
پیاپی 46 (تابستان 1402)

The excessive emission of greenhouse gases in recent decades and the ongoing changes in the climate have changed the meteorological parameters and, accordingly, the climatic zones. In this study, the future prospect of climatic zones and the risk of desertification in the watershed of Karun basin was investigated using UNEP index and LARS-WG6 microdirection model and HadGEM2-ES model under the RCPs emission scenarios for three periods 2021-2040, 2041-2060, and 2061-2080. The results pertaining to all three future periods and RCPs release scenarios showed that the long-term average annual precipitation will decrease between 1.9 and 14.6% compared to the base period, but the annual average minimum temperature will be between 1.2 and 3.4 °C, maximum temperature between 1.3 and 3.7 °C and the annual average of evaporation and transpiration will increase between 4.7 and 12.3% compared to the observation period. In the upcoming period and based on the emission scenarios, dry climate (the risk of very severe desertification) and semi-arid climate (the danger of severe desertification) increase 3.5% and 4.4%, respectively, and semi-humid (moderate desertification) and humid (no desertification) and very humid (moisture and wet climate) decrease 4% and 4.7%, respectively. However, semi-humid climate zones (low risk of desertification) with 0.8% will be less severe. Under the pessimistic scenario, the semi-arid climate region will reach its maximum level among the publishing scenarios in the near future with 12.4%. Therefore, this displacement in the boundaries of climatic classification will increase the desertification of Karun basin in the upcoming period.

In this study, the efficiency of hydrological re-analyzed models SWBM, HTESSEL, HBV-SIMREG, Ensemble, and LISFLOOD in estimating the rate of evaporation from the reservoir of Yamchi dam in Ardabil was investigated. The evaporation values obtained from the re-analyzed models were validated using the findings of Penman's analytical equation and the values of eight experimental models. In addition to the methods, the accuracy of re-analyzed models was evaluated using the feed forward neural network. The resulting feed forward neural network was designed in two stages with two and three hidden layers and each was evaluated in three different combinations of network inputs. According to the findings, the values generated from the Penman analytical model had a correlation coefficient of 0.9 with the data received from studied area's evaporation pan. Among the hydrological re-analyzed models, the highest correlation with received data from study area's evaporation pan was related to LISFLOOD model with a value of 0.87 and RMSE equal to 1.37 mm per day. The obtained results showed that the mean absolute error for the LISFLOOD model with the data provided from the study area's evaporation pan was 1.14 mm per day, on a daily time scale. The results showed that in the absence of area data, re-analyzed hydrological model can readily offer the best estimate of evaporation from the reservoirs’ free surface on a monthly scale.

Migration is affected by several factors, the effect of each factor is different in various parts of the world. One of these factors is drought, which can have a greater impact upon human migration in arid and semi-arid regions. Identifying the migration centers can also affect the adopted migration policies. Standardized Precipitation Index (SPI) was used to monitor the Iran’s drought in the last three decades (1986-2016). First, using the frequency of dry months, the zoning map was obtained from the geographical information system (GIS). Then, the existence of spatial autocorrelation in the data was investigated using Global Moran's I statistics. In the next step, the Hot Spot analysis on the data of the net migration rate was done to identify the migration centers. The findings showed that the spatial pattern of occurrence of dry months during the last three decades increases from the south and southeast of Iran to the north and west. Moreover, the values of Moran's I for the net migration rate in the studied periods were between 0.17 and 0.45, which indicates clustering in the migration data. The hot spot analysis also showed that the centers of migration in the country are located in the center and west of the country. Comparing migration centers with the pattern of dry months of the year shows that migration centers, both in-migration and out-migration, are located in places with moderate to high zones of dry months, which can make complicated in identifying the relationship between drought and migration flows.

One of the most important atmospheric challenges in recent decades in metropolitan areas is air pollution, which is caused by various natural and human factors and has harmful impacts on humans and the environment. Accordingly, investigating air pollution is important and necessary. For estimating the amount of carbon monoxide and nitrogen dioxide, as well as water vapor in the atmosphere in southern and southwestern provinces in 2018-2019, the data of Sentinel-5 satellite images was used. The findings showed the maximum concentration of Co with the value of 0.037 mol/m^2 in April 2019, the maximum concentration of H2O with the value of 3703 mol/m^2 in August 2019 and the maximum concentration of NO2 with the value of 0.000188 mol/m^2 in November 2018. The maximum daily LST value was 324.5 degrees Kelvin in June 2019 and the maximum nighttime LST value was 302.5 degrees Kelvin in June 2019. The maximum thickness of the optical depth of aerosols with a value of 13.79 μg/m^3 at a wavelength (0.47 μm) was in July 2019 and its lowest value with a value of 1.57 μg/m^3 in a wavelength of (55 /0 μm) was in November 2018. The results of temporal and spatial monitoring of CO, NO2, H2O, LST and AOD values give the possibility of a more concrete understanding of spatial and temporal changes of the examined components on a regional macro scale.

The relationship between air quality and the spread of Covid-19 has been proven to be sometimes positive and sometimes negative in previous studies. A research gap was seen in Iran regarding the impact of air quality on the spread of Covid-19. Thus, this study examined the relationship between the reproductive number of Covid-19 with the air quality index and meteorological factors. The reproductive number is an index to explain the spread of a viral disease. The air quality index related to each of the pollutants NO2, O3, CO, PM2.5, PM10, and the criterion pollutant were utilized in this study. Meteorological factors, including temperature, relative humidity, precipitation, air pressure, and wind speed were used. The data were obtained weekly and at Tehran province. The dependence of variables was determined using Pearson and Spearman correlation coefficients. The examined relationships were then estimated by regression. Each independent variable was tested in a distinct regression because of the limitations of the study. The linear and curvature regressions were performed according to the dispersion of variables and statistical assumptions. The best model was fitted based on statistical significance and regression coefficient of determination. The results of correlation tests showed the dependence of reproductive number with PM10, PM2.5, SO2 and relative humidity. Regression tests also confirmed the relationship between the reproductive number (dependent variable) and each of the independent variables PM10 (coefficient of determination 0.274), PM2.5 (coefficient of determination 0.358), SO2 (coefficient of determination 0.359), and relative humidity (determination coefficient 0.213). The results indicated that an increase in PM10, PM2.5, SO2 and decrease in relative humidity was associated with an increase in spread of Covid-19.

Like any other complex system, cities fragment when they are not properly managed. Thus, the fragility conditions should be understood to calculate the effective strategies in responses. This study aims to explain the factors of fragility in the metropolitan city of Tehran using structural approach. This field study seeks to identify the factors effective in the fragility of Tehran using a descriptive-analytical approach. In order to formulate the theoretical framework, the related literature was reviewed using the documentation method. Then, experimental data were extracted based on the environmental scanning technique. The population included 14 experts and specialists in the field of urban planning, who were selected based on purposive sampling. To this aim, 91 factors were extracted as primary indicators of urban fragility based on the theoretical literature. Then, 51 factors with a lower percentage of consensuses were eliminated and 40 ones remained based on experts’ opinions taken through a questionnaire. In the next step, the answers were analyzed and evaluated applying structural mutual effects analysis in MicMac software, resulting in determining the degree of direct and indirect influence of the factors on each other and on the fragility process of Tehran. According to the results, 20 factors play a significant role in the fragility of Tehran among 40 primary influencing ones. The above-mentioned factors are rapid growth of urbanization, concentrated poverty, widespread financial corruption, social and gender inequality, unemployment rate, political instability, epidemic diseases, informal settlements, urban violence, economic security, people's participation, lack of basic urban services, exposure to natural disasters, social security, income inequality, improper distribution of security-development capacities, sanctions, global economic shocks, real insecurity, and sudden price shocks.

Today, it is so important to investigate how to deal with natural disaster in making communities and cities resilient in all disciplines. In the natural disasters of recent years, along with government assistance, non-governmental organizations have played a very important role in bringing people back to normal life quickly and repairing the damage. The quick impact of natural disasters leads to numerous damages, that’s why it is necessary to pay attention to the role of non-governmental organizations in effectively confronting people with floods. Therefore, this applied and descriptive-analytical research was conducted with the aim of assessing the situation of non-governmental organization in increasing social resilience after the flood crisis. The study population consists urbanism experts and non-governmental organizations. A total of 49 questionnaires were distributed between them. For data analysis, two sample independent t-test and path analysis of structural equations in SPSS and SmartPLS software were used. The results showed that social responsibility (0.890), trust (0.879), empowerment of individuals (0.879) scored the highest values, and educational spaces (0.663), emproving the quality of the environment (0.722), and providing voluntary experiences (0.746) showed lowest scores in the face of resilience. Therefore, non-governmental organizations can be effective in increasing social resilience after future crises by creating and improving educational spaces, improving the quality of the environment and providing voluntary experiences. Moreover, the results of two sample independent t-test showed that the desired dimensions are at the desired level (p<0.05). The responses of the two statistical communities of urbanism experts and non-governmental organizations in evaluating the role of non-governmental organizations in increasing social resilience after the flood crisis are the same to the variables in the response of the urbanism experts and non-governmental organizations.

Environmental hazards invariably lead to significant human and financial repercussions, underscoring the crucial need to thoroughly investigate and recognize hazardous areas. Recognizing the importance of this matter, this study focuses on identifying regions susceptible to natural hazards within Roodehen’s urban area. To achieve the objectives, a variety of data sources, including a digital model with a 30-meter height resolution, a geological map at a scale of 1:100,000, a topographic map at a scale of 1:50,000, and additional information layers were harnessed as primary research tools. The research method integrated multiple analytical tools, incorporating a digital model with a 30-meter height resolution, a geological map at a scale of 1:100,000, a topographic map at a scale of 1:50,000, and various information layers. ArcGIS software served as a pivotal component in data analysis, bolstered by the fuzzy logic model and the ANP model. The approach encompassed a multi-step process commencing with the utilization of the integrated fuzzy logic and ANP model, driven by diverse parameters, to pinpoint areas susceptible to flood hazards, amplitude movements, and earthquake vulnerability. Consequently, a comprehensive hazard map of Roodehen’s urban area was formulated based on the obtained outcomes. The study underscores Roodehen's urban area as being at a heightened risk of natural hazards. Particularly, the western and northern regions exhibit elevated vulnerability due to their proximity to fault lines, earthquake centers, and steep slopes, making them susceptible to slope movements and earthquake hazards. Conversely, the central parts of the region, situated in close proximity to the river with low slopes and altitude, face a higher risk of flooding, and demonstrate significant earthquake vulnerability.