Search published articles
Showing 105 results for Ran
Roya Poorkarim, Hossein Asakereh, Abdollah Faraji, Mahmood Khosravi,
Volume 9, Issue 4 (3-2023)
Volume 9, Issue 4 (3-2023)
Abstract
In the present study, the data of the ECMWF for a period of 1979 to 2018 was adopted to analyze the long term changes (trends) of the number of cyclones centers of the Mediterranean Sea.There are many methods (e.g. parametric and non- parametric) for examining changes and trends in a given dataset. The linear regression method is of parametric category and the most common nonparametric method is Mann-Kendall test. By fitting the Mann-kendall model and the linear regression model, the frequency of the cyclone centers of the Mediterranean basin was evaluated in seasonal and annual time scales. Analyzing the trend of changes of the number of cyclone centers on a seasonal scale showed that the five-day duration have had a significant trend in spring, autumn and summer. Whilest on an annual scale, there was no significant trend in any of the duration. By fitting the regression model on seasonal and annual scale, one- and two-day duration have a positive regression line slop.
Ms. Sousan Heidari, Dr. Mostafa Karimi, Dr. Ghasem Azizi, Dr. Aliakbar Shamsipour,
Volume 9, Issue 4 (3-2023)
Volume 9, Issue 4 (3-2023)
Abstract
Explaining the spatial patterns of drought intensities in Iran
Abstract
Recognition of spatial patterns of drought plays an important role in monitoring, predicting, confronting, reducing vulnerability, and increasing adaptation to this hazard. This study aims to identify the spatial distribution and analyze the spatial patterns of annual, seasonal, and monthly drought intensities in Iran. For this purpose, the European center Medium-Range Weather Forecast (ECMWF) data for the period 1979-2021 and the ZSI index were used to extract the drought intensities. To achieve the research goal and explain the spatial pattern of the frequency of drought intensities (Extreme, severe, moderate, and weak), spatial statistical methods such as global Moran’s I, Anselin local Moran’s Index, and hot spots were used. The results of the global Moran’s I showed that with increasing intensity, the spatial distribution of drought events has become clustered. The spatial distribution of the local Moran’s Index and hot spots also confirms this. Very clear contrast was observed in the local clusters of high (low) occurrence as well as hot (cold) spots of severe (Extreme) yearly droughts in the south, southeast, and east. In autumn, weak to Extreme droughts show a southeast-northwest pattern. But in spring and winter, the spatial pattern of drought is very strong as opposed to severe and moderate drought. Despite the relatively high variability of maximum positive spatial Autocorrelation of severe and Extreme monthly droughts, their spatial pattern is almost similar. The spatial clusters of severe and very severe droughts in the northwest, northeast, and especially on the Caspian coast, are a serious warning for the management of water resources, especially for precipitation-based activities, such as agriculture.
Introduction
Drought or lack of precipitation over some time is the most widespread natural hazard on the earth compared to its long-term average. This risk negatively affects various sectors such as hydropower generation, health, industry, tourism, agriculture, livestock, environment, and economy. To reduce these negative or destructive effects, it must be determined how often drought occurs during the period and in which areas it is most severe. Doing so requires determining the characteristics of the drought. These characteristics include area, intensity, duration, and frequency of drought. Discovering the geographical focus, recognizing the pattern governing the frequency of occurrence and temporal-spatial distribution as well as changes in the dynamics of this hazard facilitate an important role in drought monitoring, early warning, forecasting, and dealing with these potential hazards; this information can be used to create a drought plan by providing analysts and decision-makers with ideas about drought, helping to reduce the negative and vulnerable effects and ultimately make it easier to protect or replace for greater adaptation. Many researchers have been led by these approaches to the use of statistical analysis. Numerous studies have been conducted in the study of climatic phenomena such as drought with space statistics techniques in various regions, including China, India, South Korea, and even Iran. Part of the domestic research on spatial patterns of drought is without the use of spatial statistics and a limited number of others who have used these analyzes have only studied the overall intensity of drought and have not studied the spatial patterns of different drought intensities. The main purpose of this study is to identify the distribution and spatial patterns of drought intensities in Iran using spatial analysis functions of spatial statistics based on the frequency of drought intensities (Extreme, severe, moderate, and weak) with yearly, seasonal and monthly multi-scale approach. Therefore, this study will answer the questions: a) What is the spatial distribution of drought intensity data in Iran? And b) What is the variability of spatial patterns of Iranian droughts at different time scales?
Material &Method
ERA5 monthly precipitation data for a period of 43 years from 1979 to 2021 were used for this study. an array of dimensions of 78×59×504 of data were formed in MATLAB software in which 78×59 is the number of nodes with a spatial resolution of 0.25 degrees and 504 represents the month. After creating the database, the ZSI index was used to calculate the severity of drought in annual, seasonal, and monthly comparisons. Finally, to achieve the research goal and explain the spatial pattern governing the frequency of drought intensities (Extreme, severe, moderate, and weak), spatial statistical methods such as global Moran’s I, Anselin local Moran I and hot spots was used.
Discussion of Results
Due to its ecological conditions, geographical location, and location in an arid and semi-arid region of the world, Iran is among the most vulnerable countries due to natural hazards, including drought. It has experienced many severe droughts in the last century. The occurrence of drought and its effects is one of the major challenges of water resources management in this century. The results of the Global Moran’s Index for all three annual, seasonal, and monthly scales showed a highly clustered pattern of drought events in the country. Spatial clustering of the occurrence of severe and Extreme yearly droughts in the eastern, southeastern, and southern regions is also an interesting result. These conditions are due to low precipitation and high spatial variation coefficient in these areas. This contrast of spatial clusters of drought intensities indicates the relationship between drought and temporal-spatial anomalies of precipitation so that with increasing precipitation, spatial variability of precipitation decreases, and consequently spatial homogeneity of precipitation increases. severe and moderate-intensity spots in the south-southeast in autumn and spring can be affected by fluctuations in the beginning and end of the monsoon season in South Asia due to the high variability of atmospheric circulation at the beginning and end of precipitation in these areas. Some studies have also shown the relationship between precipitation in these areas and the monsoon behavior of South Asia. Extreme drought events in winter and spring have had a positive spatial correlation pattern in the southwest, west, and northwest. However, precipitation at this time of year is concentrated in these areas. Warm clusters or concentrations of very severe drought events in the northern strip of the country, especially in the Caspian region, can be due to the high variability of precipitation at the beginning of the annual precipitation season (late summer and early autumn). Observations of these conditions in the northern strip indicate that an event with a high frequency of severe droughts, even in rainy areas, should not be unexpected. Spatial clusters of Extreme, severe, moderate, and weak drought every month using both local Moran and hot spots statistics show the fact that in Iran, the most severe droughts have occurred in the western, northwestern, and coastal areas of the Caspian Sea. However, the absence of severe droughts or spatial clusters has been the occurrence of low drought in the southeast and to some extent in the south. On a yearly scale, the south, southeast, and east have played a significant role in the spatial cluster of severe and extreme droughts. So that these areas of the country have had positive spatial solidarity. However, in these areas, negative spatial correlation prevailed in the autumn for severe drought. This may indicate an anomaly and a tendency to concentrate more precipitation in Iran, as well as many changes in seasonal and local precipitation regimes. According to the research results, a high incidence of severe and extreme drought on all three scales (monthly, seasonal and annual) even in the wettest climate of the country (northern Iran, especially the southern shores of the Caspian Sea) shows that High-intensity droughts can occur in all parts of the country, regardless of the weather conditions.
Keywords: Natural hazards, spatial patterns, Moran statistics, spatial autocorrelation, hot spots
A Mahmoud Ahmadi, J Jamal Karami,
Volume 9, Issue 4 (3-2023)
Volume 9, Issue 4 (3-2023)
Abstract
One of the most important issues that has always affected the Iranian climate and has left many socio-economic consequences and financial losses climate change is. On the other hand Sea level pressure is one of the most important climatic elements that can affect other climatic elements such as temperature, humidity and wind. The study aimed to evaluate CMIP5 models based on CORDEX and Verdai dynamics Seasonal pressure anomalies in Iran among CMIP5 models based on CORDEX project dynamic models BCC-CSM, HadGEM2-ES, GFDL and MIROC model HADGEM2-ES had a higher level of correlation and efficiency than other models.
The data of 36 synoptic milestones during the statistical period (1960-2005), the data of the HadGEM2-ES model were applied by using the CORDEX model and the RCPs scenarios for the two historical periods (1960-2005) and predicted during Three periods of near future (2040-2011), middle future (2070-2041) and distant future (2099-2071) were used. Six methods R2, MAE, MBE RMSE, t-Jacovides and t-Jacovides / R2 ratio were used to evaluate the model performance. The results showed that the model has good performance in low altitude areas. Seasonal anomalies in all seasons, scenarios and time periods studied are positive and winter shows the maximum pressure anomalies between seasons.
The maximum seasonal pressure anomaly of Iran in all seasons, scenarios and periods studied corresponds to the altitudes, including its epicenter in the Alborz and Zagros heights and high geographical offerings and the minimum pressure anomaly corresponding to low and low areas such as Khuzestan plain and The southern coast of the country.
Dr Bromand Salahi, Mrs Mahnaz Saber, Dr Abbas Mofidi,
Volume 9, Issue 4 (3-2023)
Volume 9, Issue 4 (3-2023)
Abstract
evapotranspiration is one of the most important components in water balance and management. In this research, to evaluate the effects of climate change on the amount of potential evapotranspiration in the southern part of the Aras River Basin using the downscaled data of the GFDL-ESM2M model in the CORDEX dynamic downscale under the RCP8.5 scenario during the period of 2021-2050 and its comparison. It is treated with the values of the base period (1985-2005). Data with a horizontal resolution of 22 x 22 km from the GFDL-ESM2M model were used in this research. The findings of the research showed that the minimum and maximum temperature and, accordingly, the ETp of the future period will increase compared to the base period in all six studied stations of Aras Basin (Ardebil, Ahar, Jolfa, Khoi, Mako and Pars-Abad). The value of this minimum temperature increase is estimated between 1.4 and 3.8 ºC and for the maximum temperature between 1.7 and 2.2ºC. The range of annual ETp increase varies from 133 mm to 189 mm. In the monthly ETp scale of all stations from January to July with an increase between 3.9 and 1.64 mm and from August to December with a decrease of 0.7 to 38.2 mm. Estimating the increase of ETp in the future period in the basin, especially in the months of spring, which is very important in terms of water demand, requires special attention to the possibility of this estimated increase in the planning of the water and energy sector.
Prof Bohloul Alijani,
Volume 10, Issue 1 (5-2023)
Volume 10, Issue 1 (5-2023)
Abstract
Abstract
During the recent decades the discipline of geography has lost its priority and position to some degree in Iran. Most of the graduates could not enter into the work in the universities and other organizations. The human-environment system, the main area of geographical specialty - has experienced many crises and hazards among which the global warming and climate change being the most destructive. This means that the ongoing curriculum is not working well and needs to experience a fundamental change. To implement this operation some points should be cleared out: The hazardous condition of the world and especially Iran, the education history and state of geography in Iran, and the relation between geography and sustainable development of the world. The discipline of geography has changed its approach according to the circumstances of each period several times. For example, at the beginning of the twenty-century due to the dominance of the environmental determinism, the dominant approach of geography was the relation between man and environment. But since the 1970’s the earth has encountered with different hazards and crises to the extent that it is named as the period of Anthropocene. Accordingly, the dominant approach of geography during this Anthropocene era is to identify and solve the hazards and crises and lead the man- environment system towards the sustainability as once was requested by the secretary general of the United Nation. In this regard the geography should adopt the sustainable development concepts and goals. For this reason, the geography of Iran should make a switch from its very specialized approach to a relatively wholistic view and pay more attention to the human- environment paradigm. To implement this order, the following assumptions should be considered.
- The applied objective of the discipline should be defined as “locating the suitable place for the living and activities of man without endangering the sustainability of the natural environment. This objective is not clear at the present curriculum. Defining this objective will clearly show students what is their job after finishing the career.
- The main vision of geography education is the creation of the sustainable geographical space or environment.
- The research approach is problem solving. Because most of the laws and concepts are identified and defined. Due to the hazardous nature of the earth system geographers should identify the problems and research to solve them via geographical thought and knowledge.
- The terrestrial unit for working is region. This is very important concept in geography. We cannot prescribe one sustainability procedure for all of the world. But we do one for each region. When regions became sustainable, all the world will be sustained.
- In any region the hazards and crises will be identified and described through the spatial analysis methods and will be conducted towards sustainable human – environment system. This monitoring is composed of the stages of spatial analysis, spatial planning, and spatial managing.
- All of the geography subjects and materials are necessary for sustainable development goals. The only criteria will be added is the environmental ethics in all of the geography activities and applications.
- Instructors and students should be familiar with the techniques of integration and multi-dimension modelling.
- All geography graduates will respect the nature and its resources and should consider the environmental ethics during their academic career. They should be able to identify and solve the environmental problems through the geographical thinking. Geographical thinking means asking geographical question, gathering geographical data, processing the data with geographical (spatial) methods, and presenting the results in the geographical forms, i.e., maps. All the graduates should be creative and critical and should have the power of scientific challenging and discussions.
- Geography is one independent and overarching discipline and we will offer only one total geography in bachelor level. The master career can be specialized according to the applied objectives of the societies. The doctoral program is also one integrated discipline. The specialty of graduates will be defined according to their dissertation.
- The subjects include the fundamental courses such as physical geography and sustainable development, regional courses such as the human geography of Iran, technical courses such as remote sensing, GIS, and statistics, the applied courses such as evaluating the natural resources, and so for. The students with any high school background should pass all the courses with high quality so that after graduation they have the potential to analyze the human- environment problems and recommend the required solutions.
Key words: geography curriculum, sustainable development, geography of Iran, twenty first century, environmental ethics, geographical thinking, Geography and sustainable development.
Dr Masoud Moradi, Dr Mohammad Hosein Gholizadeh, Mr Meysam Rahmani,
Volume 10, Issue 2 (9-2023)
Volume 10, Issue 2 (9-2023)
Abstract
Investigation of the Temporal and Spatial Variation of Maximum Soil Temperature in Iran
Extended Abstract
Introduction
The study of soil temperature in different depths of soil is important in climatology, hydrology, agrometeorology and water resource management. Different depths has a different temporal and spatial soil temperature variation. It represents the regional ground temperature regime. Furthermore, due to its rapid response to environmental changes, soil temperature is one of the most important indicators of climate change. The increase in soil temperature because of global warming can promotes disasters such as drought by increasing the water demand of agricultural products during the plant growth period. The increase in soil temperature also have a various consequences, include increasing evaporation from the soil surface, soil salinity in susceptible areas, which can lead to a decrease in soil yield and failure in plant growth. Therefore, knowledge of soil temperature changes in different environments is very important in climate studies. The aim of the current research is to analyze the spatial and temporal variations of soil temperature at different depths from five to 30cm of the ground and to investigate the existence of any kind of increasing or decreasing trend at different climates of Iran.
Methodology
Hourly soil temperature data (depths of 5, 10, 20 and 30 cm) were used in this research for the period of 1998-2017. The soil depth temperature is measured three times a day at 6:30 am, 12:30 pm, and 6:30 pm local time (3, 9, and 3 p.m. UTC). These data have been received for 150 synoptic stations of Iran on a daily basis from the Iran Meteorological Organization (IRIMO). IRIMO monitored the quality of soil temperature for data entry, data recording, and data reformatting errors. Data availability, discrepancies, errors, and outliers were identified during the second stage.
At the first step, temporal coefficient of variation were calculated for available soil temperature time series from five to 30 cm depths of each station. For this purpose, the average of three daily measurements of soil temperature was calculated and then the temporal coefficient of variation was obtained. In the next step, trend analysis of soil temperature has been investigated using the non-parametric Mann-Kendal test. The trend slope was calculated using Sen’s slope for each station in seasonal time scale. Trend analysis has been done for all three observations of the day.
Results and Discussion
The studied stations show significant spatial patterns in the temporal variability of soil temperature. In all four investigated depths, from five to 30 cm, the northwest parts of Iran, and some parts of Zagros and Alborz mountain ranges have high temporal coefficient of variation. In contrast, the stations located on the southern coasts and southern islands had the lowest temporal variability. In warm and cold seasons (summer and late autumn to mid-winter), the spatial changes of soil temperature at different depths are lower than spring and early autumn. However, in the warm period of the year, the soil temperature experiences lower spatial variations at different depths. Spring and autumn seasons, as the transition period from cold to warm and warm to cold seasons, show the most spatial temperature variations in Iran. Detected trends do not have significant differences among the three observations of the day. Soil temperature Trend analysis at different depths showed positive values for two seasons of summer and winter over most of the stations throughout Iran. Extreme trends are more frequent in the summertime of Zagros and Alborz mountainous regions, while in the winter season the stations located at the southern latitudes of Iran have experienced the most positive trends. In the summer season, higher trends with 99% confidence are more frequent in the mountainous areas. These positive trends in soil temperature have occurred in all studied depths. The negative trend at different depths is a distinct feature of the autumn season, which is significantly more prevalent than other seasons throughout Iran. The analysis of soil temperature trends in different depths shows that values above 1 degree Celsius often occur in 5 to 20 cm deeps. The increasing trend of soil temperature in winter shows a greater spatial expansion, which is indicate increasing annual minimum soil temperatures and the increasing trend of Iran's soil temperature.
Keywords: Soil Temperature, Spatiotemporal Variations, Man-Kendal Test, Sen's Slope, Iran
Seddigheh Farhood, Asadollah Khoorani, Abbas Eftekharian,
Volume 10, Issue 2 (9-2023)
Volume 10, Issue 2 (9-2023)
Abstract
Introduction
In recent years, research on climate change has increased due to its economic and social importance and the damages of increasing extreme events. In most studies related to climate change, detecting potential trends in the long-term average of climate variables have been proposed, while studying the spatio-temporal variability of extreme events is also important. Expert Team on Climate Change Detection and Indices (ETCCDI) has proposed several climate indices for daily temperature and precipitation data in order to determine climate variability and changes based on R package.
Various methods have been presented to investigate changes and trends in precipitation and temperature time series, which are divided into two statistical categories, parametric and non-parametric. The most common non-parametric method is the Mann-Kendall trend test. One of the main issues of this research is the estimation of each index value in different return periods. The return period is the reverse of probability, and it is the number of years between the occurrence of two similar events (Kamri and Nouri, 2015). Accordingly, choosing the best probability distribution function is of particular importance in meteorology and hydrology.
Despite of the enormous previous studies, there is no comprehensive research on the estimation of extreme indices values for different return periods. Accordingly, this study focuses on two main goals: First, the changes in temperature and rainfall intensity are analyzed by analyzing the findings obtained from extreme climate indices (15 indices) and then (second) estimating the values of the indicators for three different return periods (50, 200 and 500 years).
Data and methods
In this research, the daily data of maximum, minimum and total annual precipitation of 49 synoptic stations for 1991-2020 were used to analyze 15 extreme indices of precipitation and temperature. Namely, FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0oC; SU, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25oC, ID, Number of icing days: Annual count of days when TX (daily maximum temperature) < 0oC; TXx, Monthly maximum value of daily maximum temperature; TNx, Monthly maximum value of daily minimum temperature; TXn, Monthly minimum value of daily maximum temperature; TNn, Monthly minimum value of daily minimum temperature; DTR, Daily temperature range: Monthly mean difference between TX and TN; Rx1day, Monthly maximum 1-day precipitation; Rx5day, Monthly maximum consecutive 5-day precipitation; SDII Simple precipitation intensity index; R10mm Annual count of days when PRCP≥ 10mm; R20mm Annual count of days when PRCP≥ 20mm; CDD. Maximum length of dry spell, maximum number of consecutive days with RR < 1mm; CWD. Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm. Finally, the trends of indices were estimated using the non-parametric Mann-Kendall test and the values of these indicators were estimated for 50, 200 and 500 years return periods.
In order to calculate values of each indicator for a given return period, the annual time series and its probability of occurrence are estimated and the most appropriate statistical distribution function that can be fitted on the data is selected from among twelve functions. In this estimation, EASY-FIT (a hydrology software), which supports a higher range of distribution functions, is used. The intended significance level for 500, 200 and 50 years return periods were 0.998, 0.995 and 0.98, respectively. The functions used in this research include: Lognormal (3P), Lognormal, Normal, Log-Pearson 3, Gamma (3P), Gumbel, Pearson 5 (3P), Log-Gamma, Inv. Gaussian, Pearson 6 (4P), Pearson 6, Gamma. Kolmogorov–Smirnov test is used to assess the goodness of fit of the estimation from three return periods.
Results
The results indicate that while the trend of precipitation indices except for the Maximum length of dry spell (CDD) is decreasing, the trend of temperature indices was increasing, except for two indices of the days with daily maximum and minimum temperatures below zero degrees. From a spatial perspective, hot indices in the northwestern regions, cold indices in the southern half of the country shows an increasing trend, and the Caspian Sea, Oman Sea, Persian Gulf coastal regions, and the Zagros foothills are the most affected areas as a result of the increasing trends. Also, the index values were estimated for 50, 200 and 500 years return periods. As a result of the investigations, for temperature indices the north-west of the country has the highest values by different return periods. The increase in the values of R10, R20, RX1day and RX5day indices in the different return periods was more in the Zagros and Alborz mountain ranges, and the CWD, CDD and SDII indices have the highest values in the Caspian Sea and Persian Gulf Coastal areas.
In recent years, research on climate change has increased due to its economic and social importance and the damages of increasing extreme events. In most studies related to climate change, detecting potential trends in the long-term average of climate variables have been proposed, while studying the spatio-temporal variability of extreme events is also important. Expert Team on Climate Change Detection and Indices (ETCCDI) has proposed several climate indices for daily temperature and precipitation data in order to determine climate variability and changes based on R package.
Various methods have been presented to investigate changes and trends in precipitation and temperature time series, which are divided into two statistical categories, parametric and non-parametric. The most common non-parametric method is the Mann-Kendall trend test. One of the main issues of this research is the estimation of each index value in different return periods. The return period is the reverse of probability, and it is the number of years between the occurrence of two similar events (Kamri and Nouri, 2015). Accordingly, choosing the best probability distribution function is of particular importance in meteorology and hydrology.
Despite of the enormous previous studies, there is no comprehensive research on the estimation of extreme indices values for different return periods. Accordingly, this study focuses on two main goals: First, the changes in temperature and rainfall intensity are analyzed by analyzing the findings obtained from extreme climate indices (15 indices) and then (second) estimating the values of the indicators for three different return periods (50, 200 and 500 years).
Data and methods
In this research, the daily data of maximum, minimum and total annual precipitation of 49 synoptic stations for 1991-2020 were used to analyze 15 extreme indices of precipitation and temperature. Namely, FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0oC; SU, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25oC, ID, Number of icing days: Annual count of days when TX (daily maximum temperature) < 0oC; TXx, Monthly maximum value of daily maximum temperature; TNx, Monthly maximum value of daily minimum temperature; TXn, Monthly minimum value of daily maximum temperature; TNn, Monthly minimum value of daily minimum temperature; DTR, Daily temperature range: Monthly mean difference between TX and TN; Rx1day, Monthly maximum 1-day precipitation; Rx5day, Monthly maximum consecutive 5-day precipitation; SDII Simple precipitation intensity index; R10mm Annual count of days when PRCP≥ 10mm; R20mm Annual count of days when PRCP≥ 20mm; CDD. Maximum length of dry spell, maximum number of consecutive days with RR < 1mm; CWD. Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm. Finally, the trends of indices were estimated using the non-parametric Mann-Kendall test and the values of these indicators were estimated for 50, 200 and 500 years return periods.
In order to calculate values of each indicator for a given return period, the annual time series and its probability of occurrence are estimated and the most appropriate statistical distribution function that can be fitted on the data is selected from among twelve functions. In this estimation, EASY-FIT (a hydrology software), which supports a higher range of distribution functions, is used. The intended significance level for 500, 200 and 50 years return periods were 0.998, 0.995 and 0.98, respectively. The functions used in this research include: Lognormal (3P), Lognormal, Normal, Log-Pearson 3, Gamma (3P), Gumbel, Pearson 5 (3P), Log-Gamma, Inv. Gaussian, Pearson 6 (4P), Pearson 6, Gamma. Kolmogorov–Smirnov test is used to assess the goodness of fit of the estimation from three return periods.
Results
The results indicate that while the trend of precipitation indices except for the Maximum length of dry spell (CDD) is decreasing, the trend of temperature indices was increasing, except for two indices of the days with daily maximum and minimum temperatures below zero degrees. From a spatial perspective, hot indices in the northwestern regions, cold indices in the southern half of the country shows an increasing trend, and the Caspian Sea, Oman Sea, Persian Gulf coastal regions, and the Zagros foothills are the most affected areas as a result of the increasing trends. Also, the index values were estimated for 50, 200 and 500 years return periods. As a result of the investigations, for temperature indices the north-west of the country has the highest values by different return periods. The increase in the values of R10, R20, RX1day and RX5day indices in the different return periods was more in the Zagros and Alborz mountain ranges, and the CWD, CDD and SDII indices have the highest values in the Caspian Sea and Persian Gulf Coastal areas.
Kaveh Mohammadpour, Ali Mohammad Khorshiddoust, Gona Ahmadi,
Volume 10, Issue 2 (9-2023)
Volume 10, Issue 2 (9-2023)
Abstract
Introduction
Dust storm is a complex process affected by the earth-atmophere system. The interaction between the earth and atmosphere is in the realm of the climatologists and meteorologists, who assess atmospheric and climatic changes, and monitor dust spread. Dust is the main type of aerosols which affects directly and indirectly radiation budget. In addition, altogether they affect the temperature change, cloud formation, convection, and precipitation. The most important studies about dust analysis have considered the use of remote sensing technique and global models for analyzing the behavior and dynamics of dust in recent two decades. To achieve such a goal, this paper has used MODIS and NDDI data to study and identify the behavior of atmospheric dust in half west of Iran.
Materials and methods
The western region of Iran is the study area. The data used in this study are divided into two categories: ground-based observations in 27 synoptic stations extracted from the Iran’s Meteorological Organization during the period (1998-2010) and satellite MODIS images during the first to fourth days of July 2008 as atmospheric dust extremes. Data was analyzed by using ArcGIS and ENVI software and NDDI index.
Results and Discussion
According to results, interpolated map for the number of dusty days during the study period over the western half of Iran showed that the scope of study area does not involve an equal system aspect quantity of occurrences. The number of dusty days occurrences increase from north toward south and the sites located in northern proportions of the area have experienced lower dust events. In contrast, maximum hotspots are occurring over southwestern sites such as: Ahvaz, Ilam, Boushehr and Shiraz. Therefore, principal offspring of dust input has been out of country boundaries and arrived at distant areas. Also, based on results obtained using satellite remote sensing images and applied NDDI index, maximum of intense dust cover is observed over Fars, Ilam, Boushehr and Ahvaz provinces on the first, second, third and fourth of July. However, the lowest rate of index situated in extent far such as: East and West Azerbaijan provinces. Thus, parts located on the north of the study area experienced less dusty days and the maximum dust cores were located in the southwestern (mostly Khuzestan). The long-term results were consistent with the daily average of NDDI index in the whole study area and indicated the hotspot areas (Ilam, Ahvaz, Omidyeh, Bushehr and Shiraz) during the first to fourth days of July 2008. However, the level of dust cover in the region has reduced when a wet and cloudy synoptic system passes over the central and northwestern parts of the study area.
Conclusions
The climatic interpolated map interpretation indicated that increase of dust concentration based on ground-based stations, which are consistent with dust concentration, is overshadowed by the latitude and proximity of sources of dust source in the Middle East. Also, the long-term climatic results of ground-based observations were consistent with the NDDI index calculated on dust extremes in the whole study area and in the southern areas (Ilam, Ahvaz, Omidyeh, Bushehr and Shiraz) during study days of July, 2008. Therefore, dust occurrence increases from north to south and the maximum hotspots over southwestern confirm the proximity of the south western region of Iran to deserts and sedimentary plains and their direct relationship with dust sources in the Middle East. These regions highlight the volume and expansion of dust outbreaks, which were well detected due to the satellite imagery and spectral characteristics of MODIS for monitoring changes in the dust phenomenon.
Overall, the use of satellite remotely sensed data/images not only cover the ground-based observation datasets gap to identify, highlight, and analyse the dust phenomenon, but also takes a much more geographical approach in analysing environmental hazards such as dust. It is also suitable for studies of atmospheric compounds such as atmospheric aerosols.
Dust storm is a complex process affected by the earth-atmophere system. The interaction between the earth and atmosphere is in the realm of the climatologists and meteorologists, who assess atmospheric and climatic changes, and monitor dust spread. Dust is the main type of aerosols which affects directly and indirectly radiation budget. In addition, altogether they affect the temperature change, cloud formation, convection, and precipitation. The most important studies about dust analysis have considered the use of remote sensing technique and global models for analyzing the behavior and dynamics of dust in recent two decades. To achieve such a goal, this paper has used MODIS and NDDI data to study and identify the behavior of atmospheric dust in half west of Iran.
Materials and methods
The western region of Iran is the study area. The data used in this study are divided into two categories: ground-based observations in 27 synoptic stations extracted from the Iran’s Meteorological Organization during the period (1998-2010) and satellite MODIS images during the first to fourth days of July 2008 as atmospheric dust extremes. Data was analyzed by using ArcGIS and ENVI software and NDDI index.
Results and Discussion
According to results, interpolated map for the number of dusty days during the study period over the western half of Iran showed that the scope of study area does not involve an equal system aspect quantity of occurrences. The number of dusty days occurrences increase from north toward south and the sites located in northern proportions of the area have experienced lower dust events. In contrast, maximum hotspots are occurring over southwestern sites such as: Ahvaz, Ilam, Boushehr and Shiraz. Therefore, principal offspring of dust input has been out of country boundaries and arrived at distant areas. Also, based on results obtained using satellite remote sensing images and applied NDDI index, maximum of intense dust cover is observed over Fars, Ilam, Boushehr and Ahvaz provinces on the first, second, third and fourth of July. However, the lowest rate of index situated in extent far such as: East and West Azerbaijan provinces. Thus, parts located on the north of the study area experienced less dusty days and the maximum dust cores were located in the southwestern (mostly Khuzestan). The long-term results were consistent with the daily average of NDDI index in the whole study area and indicated the hotspot areas (Ilam, Ahvaz, Omidyeh, Bushehr and Shiraz) during the first to fourth days of July 2008. However, the level of dust cover in the region has reduced when a wet and cloudy synoptic system passes over the central and northwestern parts of the study area.
Conclusions
The climatic interpolated map interpretation indicated that increase of dust concentration based on ground-based stations, which are consistent with dust concentration, is overshadowed by the latitude and proximity of sources of dust source in the Middle East. Also, the long-term climatic results of ground-based observations were consistent with the NDDI index calculated on dust extremes in the whole study area and in the southern areas (Ilam, Ahvaz, Omidyeh, Bushehr and Shiraz) during study days of July, 2008. Therefore, dust occurrence increases from north to south and the maximum hotspots over southwestern confirm the proximity of the south western region of Iran to deserts and sedimentary plains and their direct relationship with dust sources in the Middle East. These regions highlight the volume and expansion of dust outbreaks, which were well detected due to the satellite imagery and spectral characteristics of MODIS for monitoring changes in the dust phenomenon.
Overall, the use of satellite remotely sensed data/images not only cover the ground-based observation datasets gap to identify, highlight, and analyse the dust phenomenon, but also takes a much more geographical approach in analysing environmental hazards such as dust. It is also suitable for studies of atmospheric compounds such as atmospheric aerosols.
Masoomeh Hashemi, Ezatallah Ghanavati, Ali Ahmadabadi, Oveis Torabi, Abdollah Mozafari,
Volume 10, Issue 2 (9-2023)
Volume 10, Issue 2 (9-2023)
Abstract
Introduction
Earthquakes as one of the most important natural disasters on earth, have always caused irreparable damage to human settlements in a short period of time. Severe earthquakes have led to the idea of developing an infrastructure plan to reduce the risks and damages caused by it. The urban water supply system is the most important critical infrastructure that is usually damaged by natural disasters, particularly earthquakes and floods; hence, the function of the pipelines of the water system determines the degree of resilience and design of the infrastructure against multiple natural and man-made hazards. Considering the inability to prevent earthquakes and the inability of experts to accurately predict the time it is necessary to know the status of earthquake-structure and seismicity in Tehran to determine the amount of earthquake risk in order to make the necessary planning for structural reinforcement. Theoretical and field studies of tectonic seismicity in the Tehran area show that this city is located on an earthquake-prone area around the active and important faults of Masha, north of Tehran, Rey and Kahrizak. The occurrence of 20 relatively severe earthquakes illustrates this claim. Regarding the location of faults in Tehran city, it is necessary to assess the vulnerability of Tehran water facilities.
Research Methodology
The present study is a practical-analytic one. Considering the severity of earthquake damages, it is necessary to conduct earthquake hazard zonation studies in different urban areas and to determine important indicators of damage assessment such as maximum ground acceleration, maximum ground speed, maximum ground displacement. Three indices were considered for mapping earthquake seismic zones and their integration into the GIS presented a seismic hazard map. In the analysis of earthquake risk, it is necessary to evaluate two indicators of risk and vulnerability. To prepare the general hazard power mapping the weights obtained from the ANP model were applied to the existing raster layers via the Raster Calculator command. In this way, the standardized layers are multiplied separately by their respective weights and finally overlapped. In order to evaluate the vulnerability, a series of evaluation indices are introduced and ANP techniques are used. The relative value of each index is then calculated using the multivariate approach using the SAW technique. In order to calculate the earthquake risk based on R = H * V relation, the values of these two components were multiplied. This calculation was performed in GIS software on the risk and vulnerability raster layer and the final result of this calculation was displayed on the map.
Description and interpretation of results
In this study, we tried to estimate the relative risk and risk of seismic hazard on the water supply lines in Tehran, using available data and scientific methods, and map the risk level. These lines should be prepared first by the amount of earthquake hazard risk and then by the risk map, to estimate the earthquake risk on the water supply network. first the earthquake risk then the status of the hazard lines should be calculated. The vulnerability of the water supply lines was calculated using the ANP model by multiplying the total potential hazard risk then substrate transfer network vulnerability risk map obtained transmission network. The highest risk was in the west and north of Tehran. The maps showed the risk potential and the vulnerability of the lines. These areas had high seismic potential and the density of the lines was higher in these areas. Water transmission facilities are at risk and earthquake hazards may be affected by damage to the transmission lines, drinking water to a large population will be difficult, as well as performing necessary zoning to prevent future expansion of the facility in place. These analyzes are a prelude to applying corrective techniques to pipelines to reduce their vulnerability and prevent newly created pipelines from locating in vulnerable areas. Since the results of this study are risk maps along the route of the water supply lines, so in order to prepare a risk control program, we can identify the high risk pipeline map and identify the pipeline vulnerability. And, depending on its location, provided an appropriate prevention and control plan for the conditions surrounding the pipeline environment.
Earthquakes as one of the most important natural disasters on earth, have always caused irreparable damage to human settlements in a short period of time. Severe earthquakes have led to the idea of developing an infrastructure plan to reduce the risks and damages caused by it. The urban water supply system is the most important critical infrastructure that is usually damaged by natural disasters, particularly earthquakes and floods; hence, the function of the pipelines of the water system determines the degree of resilience and design of the infrastructure against multiple natural and man-made hazards. Considering the inability to prevent earthquakes and the inability of experts to accurately predict the time it is necessary to know the status of earthquake-structure and seismicity in Tehran to determine the amount of earthquake risk in order to make the necessary planning for structural reinforcement. Theoretical and field studies of tectonic seismicity in the Tehran area show that this city is located on an earthquake-prone area around the active and important faults of Masha, north of Tehran, Rey and Kahrizak. The occurrence of 20 relatively severe earthquakes illustrates this claim. Regarding the location of faults in Tehran city, it is necessary to assess the vulnerability of Tehran water facilities.
Research Methodology
The present study is a practical-analytic one. Considering the severity of earthquake damages, it is necessary to conduct earthquake hazard zonation studies in different urban areas and to determine important indicators of damage assessment such as maximum ground acceleration, maximum ground speed, maximum ground displacement. Three indices were considered for mapping earthquake seismic zones and their integration into the GIS presented a seismic hazard map. In the analysis of earthquake risk, it is necessary to evaluate two indicators of risk and vulnerability. To prepare the general hazard power mapping the weights obtained from the ANP model were applied to the existing raster layers via the Raster Calculator command. In this way, the standardized layers are multiplied separately by their respective weights and finally overlapped. In order to evaluate the vulnerability, a series of evaluation indices are introduced and ANP techniques are used. The relative value of each index is then calculated using the multivariate approach using the SAW technique. In order to calculate the earthquake risk based on R = H * V relation, the values of these two components were multiplied. This calculation was performed in GIS software on the risk and vulnerability raster layer and the final result of this calculation was displayed on the map.
Description and interpretation of results
In this study, we tried to estimate the relative risk and risk of seismic hazard on the water supply lines in Tehran, using available data and scientific methods, and map the risk level. These lines should be prepared first by the amount of earthquake hazard risk and then by the risk map, to estimate the earthquake risk on the water supply network. first the earthquake risk then the status of the hazard lines should be calculated. The vulnerability of the water supply lines was calculated using the ANP model by multiplying the total potential hazard risk then substrate transfer network vulnerability risk map obtained transmission network. The highest risk was in the west and north of Tehran. The maps showed the risk potential and the vulnerability of the lines. These areas had high seismic potential and the density of the lines was higher in these areas. Water transmission facilities are at risk and earthquake hazards may be affected by damage to the transmission lines, drinking water to a large population will be difficult, as well as performing necessary zoning to prevent future expansion of the facility in place. These analyzes are a prelude to applying corrective techniques to pipelines to reduce their vulnerability and prevent newly created pipelines from locating in vulnerable areas. Since the results of this study are risk maps along the route of the water supply lines, so in order to prepare a risk control program, we can identify the high risk pipeline map and identify the pipeline vulnerability. And, depending on its location, provided an appropriate prevention and control plan for the conditions surrounding the pipeline environment.
Popak Dananiyani, Ehsan Soureh, Bakhtiyar Mohammamdi,
Volume 10, Issue 2 (9-2023)
Volume 10, Issue 2 (9-2023)
Abstract
Thunderstorms are one of the atmospheric phenomena; when they occur, strong winds are often reported along with heavy rains and lightning. In many cases, their occurrence is accompanied by a lot of financial and human losses. This research was carried out to investigate the Spatio-Temporal of thunderstorms and understand their trends in Iran. For this purpose, the monthly data of the number of days of thunderstorms in 201 Synoptic stations in Iran from the beginning of establishment to 2010 were used. First, the frequency of monthly and annual occurrence of thunderstorms at Synoptic stations in Iran was calculated. Also, the trend of thunderstorms was investigated based on the non-parametric Mann-Kendall test and the amount of decrease or increase of this phenomenon was determined with the help of the Sen’s slope estimator test. The results of this research showed that thunderstorms occur in all areas of Iran. However, the frequency of this phenomenon is more in the North-West, South-West, and South-East of Iran than in other parts. In terms of time, in every month of the year, part(s) of Iran is the center of the maximum occurrence of thunderstorms. For example, in the winter of southwest, south, and southeast of Iran, in the early spring of west and northwest of Iran, and the late spring of the southeast of the country, the main focus of this phenomenon has been. In the summer, northwest to the northeast of Iran and southeast and south of Fars province are the main centers of thunderstorm formation. At the beginning of the autumn season, the coasts of the Caspian Sea to the north of the Persian Gulf and towards the northwest of Iran, and in November and December, the southwest and west of Iran were the main places of occurrence of this weather phenomenon. Other results of this research showed that the trend of thunderstorms was not similar in Iran. This phenomenon showed a significant increasing trend (more than 1 day per year) at the 99% confidence level in the northwest, southwest, and southern half of Kerman province. Also, a significant decrease (0.7 days per year) was estimated in the southeast and a large part of central Iran. In other parts of Iran, a decrease or increase in thunderstorms has been observed in a scattered manner, although the amount was not significant at the 99%, 95%, and 90% confidence levels.
Nazanin Salimi , Marzban Faramarzi, Dr Mohsen Tavakoli, Dr Hasan Fathizad,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
In recent years, groundwater discharge is more than recharge, resulting in a drop-down in groundwater levels. Rangeland and forest are considered the main recharge areas of groundwater, while the most uses of these resources are done in agricultural areas. The main goal of this research is to use machine learning algorithms including random forest and Shannon's entropy function to model groundwater resources in a semi-arid rangeland in western Iran. Therefore, the layers of slope degree, slope aspect, elevation, distance from the fault, the shape of the slope, distance from the waterway, distance from the road, rainfall, lithology, and land use were prepared. After determining the weight of the parameters using Shannon's entropy function and then determining their classes, the final map of the areas with the potential of groundwater resources was modeled from the combination of the weight of the parameters and their classes. In addition, R 3.5.1 software and the randomForest package were used to run the random forest (RF) model. In this research, k-fold cross-validation was used to validate the models. Moreover, the statistical indices of MAE, RMSE, and R2 were used to evaluate the efficiency of the RF model and Shannon's entropy for finding the potential of underground water resources. The results showed that the RF model with accuracy (RMSE: 3.41, MAE: 2.85, R² = 0.825) has higher accuracy than Shannon's entropy model with accuracy (R² = 0.727, RMSE: 4.36, MAE: 3.34). The findings of the random forest model showed that most of the studied area has medium potential (26954.2 ha) and a very small area (205.61 ha) has no groundwater potential. On the other hand, the results of Shannon's entropy model showed that most of the studied area has medium potential (24633.05 ha) and a very small area (1502.1 ha) has no groundwater potential.
Nabi Mohamadi, Behrouz Sari Saraf, Hashen Rostamzadeh,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
Nowadays, due to global warming, drought and the occurrence of cold periods and heat stress, the study of climatic variables is very important. Therefore, in this research, the long-term forecast of temperature changes in northwest Iran in the base period (1985-2014) and three periods of the near future (2021-2050), the medium future (2051-2080) and the distant future (2100- 2081) was paid. For this purpose, 2 extreme temperature indices including Warm spells duration index (WSDI) and cold spells duration index (CSDI) and Maan-Kendall trend test were used to check the changes. To predict the changes of the profiles in the future period after evaluating 7 general circulation models (GCMs) from the sixth report model series (CMIP6) from two optimal models under three socio-economic forcing scenarios including SSP1-2.6, SSP3-7.0 and SSP5-8.5 was used. The spatial distribution of the trend of changes in the Warm spells duration index (WSDI) in the base period showed that its maximum core is located in the south and southwest of the region, and its amount decreases by moving towards the north and northeast. Spatial changes of the Cold spells duration index (CSDI) are characterized by its maximum cores in the western regions and around Lake Urmia and minimum cores in the central and northern regions of the study area. According to the results, the average Warm spells duration index (WSDI) and of the Cold spells duration index (CSDI) are equal to 5.53 and 3.80 days per year, respectively, and the maximum and minimum Warm spells duration index (WSDI) are 1.8 and 2.7 days, respectively Piranshahr and Parsabad stations and the maximum and minimum and the Cold spells duration index (CSDI) are also 5.7 and 1.32 days corresponding to Zarineh and Marivan stations. Examining the trend of changes also showed that in most stations, the WSDI index has an increasing trend, and this trend has become significant in some stations, but the CSDI index has a decreasing trend and is not significant in any of the stations. The evaluation of different models with different error measurement indices also showed that MRI-ESM2-0 and MPI-ESM1-2-L models have the best performance in simulating temperature extreme in the studied area. The distribution of changes in the future period also showed that the WSDI will increase in most stations and based on all three scenarios, especially the SSP5-8.5 scenario, but the CSDI trend will decrease in most stations and based on the SSP3-7.0 and SSP5-8.5 scenarios will be significant.
Hayedeh Ara, Zahra Gohari, Hadi Memarian,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
Introduction
Desertification is one of the major environmental, socio-economic problems in many countries of the world (Breckle, et.al., 2001). Desertification is actually called land degradation in dry, semi-arid and semi-humid areas, the effects of human activities being one of the most important factors (David and Nicholas, 1994). Sand areas are one of the desert landforms, whose progress and development can threaten infrastructure facilities. The timely and correct identification of the changes in the earth's surface creates a basis for a better understanding of the connections and interactions between humans and natural phenomena for better management of resources. To identify land cover changes, it is possible to use multi-temporal data and quantitative analysis of these data at different times (Lu, et.al., 2004), therefore, one of the accurate management tools that causes the application of management based on current knowledge, these studies Monitoring is done using the mentioned data. The use of satellite data and ground information in such studies has caused many temporal and spatial changes of phenomena to be well depicted, which can be beneficial in better understanding and interaction with the environment and ultimately its sustainable management and development. To obtain and extract basic information, the best tool is to use telemetry technologies, which by using satellite data, in addition to reducing costs, increases accuracy and speed, and its importance is increasing day by day in the direction of sustainable development (Alavi Panah, 1385). Since field studies in the field of spatial changes of sandy areas of this city are difficult and expensive to repeat, facilities such as simulating these areas with expert algorithms and artificial intelligence can be used to investigate and monitor critical areas at regular intervals. Accurate and economically appropriate. Therefore, in this research, with the aim of investigating the effectiveness of these models in the periodic changes of the sandy plains of Ferkhes plain, two algorithms, perceptron neural network and random forest, were chosen, and the reason for choosing these models is the ability to model according to the existing uncertainties, interference Fewer users and insensitivity of the model to how the data is distributed.
Materials and Methods
The progress and development of the sandy areas of the Fern Plain depends on three factors, climatic, environmental and human. Therefore, the input variables to the expert and artificial intelligence models were chosen to cover these three factors. Therefore, factors such as drought, the number of dusty days, as well as vegetation index were entered into the model as dynamic variables, and environmental factors such as soil, elevation and altitude, geology, slope and direction were entered into the model as static variables. The statistical period investigated for the changes of wind erosion zones was considered to be 15 years from 2000 to 2015, based on this time base, qualitatively homogeneous and reconstructed meteorological data and images A satellite was selected and processed in 5-year periods (2000, 2005, 2010 and 2015). Modeling of the changes of sandy areas was done using two algorithms of perceptron neural network and random forest in MATLAB software environment. To choose the best neural network structure, a large number of neural networks with different structures were designed and evaluated. These neural networks were built and implemented by changing adjustable parameters (including transfer function, learning rule, number of middle layer, number of neurons of middle layer, number of patterns). One of the most common types of neural networks is multilayer perceptron (MLP). This network consists of an input layer, one or more hidden layers and an output. MLP can be trained by a back propagation algorithm. Typically, MLP is organized as a set of interconnected layers of input, hidden, and output artificial. The accuracy of these networks was checked by the statistical criteria calculated in the test stage, and finally the network that had the closest result to the reality was selected as the main network. The main active function used in this research is sigmoid, which is a logistic function. Then by comparing the network output and the actual output, the error value is calculated, this error is returned in the form of back propagation (BP) in the network to reset the connecting weights of the nodes (Chang and Liao, 2012). Other evaluation indices MSE, RMSE and R were used as network performance criteria in training and validation. The selection of Fern plain as a study area is due to the high potential of this area in the advancement of sand areas, for this purpose, 8 effective factors in the development of these areas were investigated. These factors were entered into the model in the form of three dynamic indices and five static indices.
Results and Discussion
In evaluating the results of modeling algorithms, dynamic variables in all periods were introduced as the most important factors in the occurrence of wind erosion and the advancement of sand areas. The diagram of the importance of predictor variables is presented in Figure 7. The results show that the vegetation cover index ranks first in all periods, the drought index ranks second in 2000 and 2015, and the dust days index ranks third in these two years. Meanwhile, in 2005 and 2010, the dust index and drought index ranked second and third respectively. Among the static variables used in this research, the height digital model variable was ranked fourth in 2000 and 2010, and in 2005 and 2015, geological and soil variables were important. In almost all studied periods, the direction factor is less important than other factors, which can be removed from the set of variables required for modeling to predict sand areas.
Desertification is one of the major environmental, socio-economic problems in many countries of the world (Breckle, et.al., 2001). Desertification is actually called land degradation in dry, semi-arid and semi-humid areas, the effects of human activities being one of the most important factors (David and Nicholas, 1994). Sand areas are one of the desert landforms, whose progress and development can threaten infrastructure facilities. The timely and correct identification of the changes in the earth's surface creates a basis for a better understanding of the connections and interactions between humans and natural phenomena for better management of resources. To identify land cover changes, it is possible to use multi-temporal data and quantitative analysis of these data at different times (Lu, et.al., 2004), therefore, one of the accurate management tools that causes the application of management based on current knowledge, these studies Monitoring is done using the mentioned data. The use of satellite data and ground information in such studies has caused many temporal and spatial changes of phenomena to be well depicted, which can be beneficial in better understanding and interaction with the environment and ultimately its sustainable management and development. To obtain and extract basic information, the best tool is to use telemetry technologies, which by using satellite data, in addition to reducing costs, increases accuracy and speed, and its importance is increasing day by day in the direction of sustainable development (Alavi Panah, 1385). Since field studies in the field of spatial changes of sandy areas of this city are difficult and expensive to repeat, facilities such as simulating these areas with expert algorithms and artificial intelligence can be used to investigate and monitor critical areas at regular intervals. Accurate and economically appropriate. Therefore, in this research, with the aim of investigating the effectiveness of these models in the periodic changes of the sandy plains of Ferkhes plain, two algorithms, perceptron neural network and random forest, were chosen, and the reason for choosing these models is the ability to model according to the existing uncertainties, interference Fewer users and insensitivity of the model to how the data is distributed.
Materials and Methods
The progress and development of the sandy areas of the Fern Plain depends on three factors, climatic, environmental and human. Therefore, the input variables to the expert and artificial intelligence models were chosen to cover these three factors. Therefore, factors such as drought, the number of dusty days, as well as vegetation index were entered into the model as dynamic variables, and environmental factors such as soil, elevation and altitude, geology, slope and direction were entered into the model as static variables. The statistical period investigated for the changes of wind erosion zones was considered to be 15 years from 2000 to 2015, based on this time base, qualitatively homogeneous and reconstructed meteorological data and images A satellite was selected and processed in 5-year periods (2000, 2005, 2010 and 2015). Modeling of the changes of sandy areas was done using two algorithms of perceptron neural network and random forest in MATLAB software environment. To choose the best neural network structure, a large number of neural networks with different structures were designed and evaluated. These neural networks were built and implemented by changing adjustable parameters (including transfer function, learning rule, number of middle layer, number of neurons of middle layer, number of patterns). One of the most common types of neural networks is multilayer perceptron (MLP). This network consists of an input layer, one or more hidden layers and an output. MLP can be trained by a back propagation algorithm. Typically, MLP is organized as a set of interconnected layers of input, hidden, and output artificial. The accuracy of these networks was checked by the statistical criteria calculated in the test stage, and finally the network that had the closest result to the reality was selected as the main network. The main active function used in this research is sigmoid, which is a logistic function. Then by comparing the network output and the actual output, the error value is calculated, this error is returned in the form of back propagation (BP) in the network to reset the connecting weights of the nodes (Chang and Liao, 2012). Other evaluation indices MSE, RMSE and R were used as network performance criteria in training and validation. The selection of Fern plain as a study area is due to the high potential of this area in the advancement of sand areas, for this purpose, 8 effective factors in the development of these areas were investigated. These factors were entered into the model in the form of three dynamic indices and five static indices.
Results and Discussion
In evaluating the results of modeling algorithms, dynamic variables in all periods were introduced as the most important factors in the occurrence of wind erosion and the advancement of sand areas. The diagram of the importance of predictor variables is presented in Figure 7. The results show that the vegetation cover index ranks first in all periods, the drought index ranks second in 2000 and 2015, and the dust days index ranks third in these two years. Meanwhile, in 2005 and 2010, the dust index and drought index ranked second and third respectively. Among the static variables used in this research, the height digital model variable was ranked fourth in 2000 and 2010, and in 2005 and 2015, geological and soil variables were important. In almost all studied periods, the direction factor is less important than other factors, which can be removed from the set of variables required for modeling to predict sand areas.
Javad Sadidi, Hassan Ahmadi, . Ramin Rezae Shahabi, Amir Pishva, Omid Kheyri, Godratallah Nooraie,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
The pervasiveness of the concept of vulnerability in various dimensions has led to the emergence of the theory of vulnerability in the spatial sciences. According to the theory of vulnerability, in any given space, there is a coefficient of vulnerability, while the levels and amplitude of safety are not evenly distributed on the surface of that space. Residential use is one of the most important and main uses in the urban land use system, and safety management and attention to its defense requirements are very important due to the high population density in large cities. The present study is in the field of assessing the vulnerability of residential uses against external threats with a passive urban defense approach in District 10 of Tehran, which was conducted in the form of spatial studies and by implementing an analytical model in three steps. First, the principles and requirements of passive defense were identified and classified into three groups of structural, demographic and spatial parameters, and using the questionnaire and expert survey tools, the priorities of passive defense principles in relation to residential spaces were determined. Then, based on the network analysis process, the weight of each criterion was determined and the weight of the ANP model was applied to the spatial layers of the region in ArcGIS software. The results of the model showed that in terms of structural indicators, more than 78% of residential units in the region are in the group of structures with high vulnerability and in terms of demographic indicators, in 88% of residential units in case of external threats, the level of vulnerability is high. In terms of spatial indicators, more than 92% of residential spaces are adjacent to several incompatible uses and have the highest vulnerability. In general, the results of overlapping layers showed that more than 86% of residential units in the area are located in vulnerable zones and the vulnerability of residential units in these zones is very high.
Hossein Hataminejad, Alireza Sadeghi,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
Measuring urban resilience can help develop appropriate strategies and policies for cities facing unexpected shocks and their consequences. Since urban resilience is a complex concept and difficult to operationalize, developing a technique or method to actualize this concept is a major milestone in understanding the factors and interactions that help create and maintain resilience. Tehran's metropolis has a high concentration of industries, government organizations, services, and facilities, which makes its management very complicated when a natural disaster occurs. Previous conditions or inherent socio-economic characteristics show that Tehran is not immune from flood forces. In fact, it is important to measure resilience against urban disasters for areas located on rivers in Tehran due to its inherent characteristics and spatial-temporal changes of floods in the region. This research focuses on measuring the resilience of the areas located on the rivers of Tehran. The measurement approach is based on creating a composite index based on six dimensions of social, economic, institutional, infrastructure, social capital, and environmental resilience against floods. This research has been done by developing a mixed multi-criteria decision-making method. The AHP model has been used for prioritizing the selected indicators and the TOPSIS model has been used to rank the areas located on the rivers of Tehran city based on their resilience levels. The results show that region 22 is the most resilient region, while regions 4, 5, and 14 have the lowest resilience levels. The findings of this research can help urban planning organizations such as Tehran Research Planning Center to integrate disaster resilience in urban planning and change from reactive plans to preventive urban adaptive strategies such as risk-sensitive urban land use planning.
Tajdin Karami, Ali Shamaei, Fateme Mohebi,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Abstract
Ecological resilience is a concept that implies the reversibility of ecological structures and functions against the shocks experienced. The northern zone of Tehran, as the most important ecological support of this city, has undergone many land-use changes in recent decades. The present study has analyzed the role of land-use change in the ecological resilience of green infrastructure (as one of the pillars of ecological structure) in District 1 of Tehran Municipality. This study is an applied one in terms of purpose and is considered a descriptive-analytical one in terms of the method used. In this study Landsat satellite data (1976-2021) were used to detect the changes of interest, and landscape metrics were used to analyze the ecological resilience conditions. Based on the results of this study period, the Number of Patches (NP) has significantly increased and the Class Area (CA) has decreased during the period covered by this study. These changes indicate the fragmentation process and loss of structural cohesion of the green patches. The measurement results for the connectivity metrics (ENN and GYRATE) also showed a small connectivity between the green patches in the area. In addition, the results for CONTAG (Contagion Landscape metrics) measure indicated that, due to low connectivity, the transmission rate is low. Therefore, it can be said that the green infrastructure of the region has lost its structural cohesion in the face of land-use change, and as a result, the expected ecological functions and services have also failed. According to the results, the green infrastructure of the study area is vulnerable to land-use changes and their ecological resilience has been significantly reduced.
Dr. Habibollah Fasihi, Dr. Taher Parizadi,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Urban planners consider historical fabric as the beating heart of cities. However, cities and specifically their historical fabrics are constantly under influence of natural and human-induced hazards. This study aimed to assess the vulnerability of Ferdowsi neighborhood as an example of Tehran’s historical fabric. The data was obtained from geographical information system (GIS) files and a survey, as well as historical contexts and documents were analyzed as part of this study. Ten municipal experts were also asked to provide a score of 1 to 5 for each of the 29 indicators compared to standard levels. The mean value of these scores was then used to evaluate the role of each parameter in the vulnerability of this neighborhood. Study findings indicate a high potential for the occurrence of natural and human-induced hazards in the study area. The abundance of unstable multistory buildings and derelict electricity and water networks, storage of flammables in warehouses, and lack of sewage network for half of the buildings were the most significant factors contributing to the vulnerability of this ancient fabric. Problems such as insecurity, drug abuse in public spaces, overcrowding and daytime congestion have also led to rapid relocation of neighborhood residents, who subsequently were replaced by commercial activities and warehouses. In conclusion, numerous problems are contributing to the vulnerability of historical fabric of this neighborhood and such problems are highly likely to be applied to other urban historical fabrics in Tehran.
Understanding the mechanism of the atmospheric anomalies governing the summer dust in northeast Iran
Ms. Tahmineh Chehreara, Miss Somayeh Hajivand Paydari,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Identification of dust centers and, of course, the behavior of this phenomenon in different regions creates one of the problems of the last few decades, which is investigated as a hazard. To this end, statistics from 15 meteorological stations in the northeastern region of Iran, including North Khorasan, Razavi Khorasan, and South Khorasan provinces, were used over a 17-year period (2016-2000). To clarify the mechanisms governing dusty days, the meridional and zonal wind components and geopotential height were obtained by referring to the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR). HYSPLIT model and MODIS AOD values were used to track and identify dust centers. The results showed that during the warm season, due to the establishment of a strong quasi-stationary blocking system in the lower levels of the atmosphere, negative vorticity increased in the maximum air descent area, ultimately leading to the dominance of a northern flow for the region. Anomalies in geopotential height and vorticity were identified, and three dominant abnormal patterns were found in the occurrence of maximum dust storms in the region. An increase in geopotential height of more than 5 to 10 geopotential meters and an increase in negative vorticity are considered major conditions. By examining the tracking model and using satellite data, five main centers that affect over 90% of the region's dust storms were identified, among which Turkmenistan has a significant role with two separate centers and one common center with Uzbekistan in the occurrence of summer dust storms in northeastern Iran.
Gholam Hassan Jafari, Zeinab Karimi,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Abstract
In geosciences, morphotectonic indicators are used to investigate the effectiveness of land surfaces from neotectonic activities. In this article, the results of morphotectonic indices by tectonic zones of Iran, according to the energy released from the earthquake of 1900-2009 and the position of the basins relative to the types of faults (young seismic faults), Quaternary and pre-Quaternary) were analyzed. For this purpose, 110 years old Iran seismic data was extracted from the geodatabase, and during the programming process in MATLAB, it was converted from point-vector to surface-raster. In addition the results of the evaluation of morphotectonic indices of 142 basins of different zones were used; 8 inactive basins, 40 semi- active basins, and 94 active basins. Inactive basins are located in Alborz, Zagros, and Central Iran. . The results indicate that the amount of energy released can't examine a significant role in evaluating the morphotectonic indices of the basins. Basin’s location in the area of Quaternary faults and young seismic is of great value in the tectonically active basin. The lie of semi-active basins adjacent to active basins, or the lie of inactive basins adjacent to semi-active and active basins; and it should be borne in mind that the thresholds used to estimate the tectonic activity status of basins cannot be used as a definite and mathematical criterion in estimating the tectonic status of basins.
Mrs Mozhgan Shahriyari, Dr Mostafa Karampoor, Dr Hoshang Ghaemi, Dr Dariush Yarahmadi, Dr Mohammad Moradi,
Volume 11, Issue 1 (5-2024)
Volume 11, Issue 1 (5-2024)
Abstract
Flash floods are one of the most dangerous natural events and often cause loss of life and damage to infrastructure and the environment. This research investigated the occurrence of the most intense continuous monthly floods (October-March) from 1989 to 2021. Precipitation data from 115 synoptic stations were selected. Then, the total rainfall of 1 to 9 days was sorted according to intensity. Using Minitab statistical software and the Andersen-Darling index, heavy rains were extracted based on the 95th percentile. Then, based on the criteria of the highest and lowest number of rainy days, the highest and lowest accumulated rainfall, the wettest and driest months were determined. Considering the three criteria of intensity, continuity, and rainfall coverage, the strongest storms in the wettest months were selected. The data used for synoptic analysis include the average sea level pressure data, the height and vertical component of the wind at 500 hPa, the wind and humidity field specific to the pressure levels 925, 850, and 700 hPa, and the horizontal moisture flux values specific to the pressure level 925, 850 and 700 hPa. The probability of the occurrence of atmospheric rivers was identified by the moisture flux extracted from the specific, meridional, and meridional wind components. The results showed that the storms of October 27-31, 2015, November 5-7, 1994, December 12-16, 1991, January 11-15, 2004, February 3-9, 1993, and March 13-15, 1996 were the strongest in the wettest months. During the storms of October, November, February, and March, moisture has been transported from the southwest of the Red Sea by atmospheric rivers to the western, southwestern, southern, and southeastern regions of Iran.
© 2024 CC BY-NC 4.0 | Journal of Spatial Analysis Environmental hazarts
Designed & Developed by : Yektaweb