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Ebrahim Moghimi, Alireza Salehipour Milani, Mehdi Chakeri, Mostafa Moghimi,
Volume 1, Issue 2 (7-2014)
Volume 1, Issue 2 (7-2014)
Abstract
In the Tsunami of Dec. 26, 2004, although there was a large distance between the earthquake center of Indian Ocean and coastal cities of Iran, the Tsunami waves brought some damages in Chabahar coast. This means that if the earthquake center was closer to Iran, Iran’s coastal regions would have confronted serious danger... In the present study, we used ComMIT software (Community Model Interface for Tsunami) as a research tool, Inundation modeling was done for the Jask coastal area in order to assess the potential and find out the impact of tsunami from any future Makran Subduction Zone earthquake.
Computer modellings programs help analyze sea-level data to generate forecasts of tsunami wave height and the expected inundation for specific coastal areas. In this research we use ComMIT software. For modeling the wave, the assumption for 10*100 Km blocks is applied. In this formulation, the approximate for wave behavior is represented for coastal regions. In this modeling, three steps of gridding with different preciseness are used. By assuming an earthquake by magnitude of 8.6 Richter and movement of 6 blocks mentioned in the subduction area close to Jask, each block rises about 16 m. the first impact by coast by a wave of 2 m height knocks up the coast at 26 minutes.
According to the performed modeling, the first change after Tsunami is reduction in sea level and this procedure continues till 11 minutes after that. In this time, the maximum amount of water regress from coastal line for 2 m is observed. After this time, the first wave attacks Jask. The first wave impacts Jask headland and then affects the whole eastern coast of Jask. The wave height in this region in 26 minutes after Tsunami reaches to 2 m and starts approaching in the coast. This process occurs in the western coast of Jask by a 10-minute delay. The first tall wave impacts western coast at 36 minutes after Tsunami and regresses for about 2.5 Meter. The second wave is ready to attack the western coast and 40 minutes after Tsunami the second wave attacks eastern coast. The height of this wave in its maximum is about 2.5 Meter.
According to investigation of existing models concerning influence of Jask city by Tsunami is divided to three sections: A) eastern coast of Jask, B) Jask headland, C: western coast. The eastern coast us the first region that is confronting attack of the greatest waves and the maximum rate of wave march in the coast (about 1 Km) is in this region. In this area the slope is about 1% and concerning topography, it provides an appropriate condition for wave on the coast. In eastern coast of Jask, there is an intense concentration of governmental offices and military centers including Jask airport and Admiral Force’s quay. In the first waves caused by Tsunami, these installations would be damaged severely and in second and third waves this process continues.
Dr Mohammad Ghasem Torkashvand,
Volume 5, Issue 2 (9-2018)
Volume 5, Issue 2 (9-2018)
Abstract
Dust phenomenon is a natural occurrence that occurs widespread in arid and semi-arid regions of the world, especially in the sub-equatorial latitudes. This phenomenon is among the greatest environmental problems in the world. The release of this destructive climatic phenomenon in a scattered manner in the atmosphere varies in size, time and concentration. Since this phenomenon is influenced by the specific conditions of climate effects, its effects may continue to be as close as 16,000 kilometers from the source and cause abnormal environmental effects on the one hand, and numerous damage to agriculture, industry, transportation and telecommunication systems on the other hand. Dust storms, as an atmospheric destructive phenomenon, have created adverse environmental impacts for the west of Iran and caused many problems for the inhabitants of this region. Therefore, studying this phenomenon is necessary in order to achieve a comprehensive approach to deal with it. The present study was conducted with the aim of identifying the instantaneous atmospheric conditions, conduction and source of the dust storms with a synoptic modeling approach.
In this study, in order to investigate the dust storms structure in the southwest of Iran, the dust storm occurred on May 15, 2015 was selected. The reason for choosing the present day, based on reports from the Observatory and Monitoring Center of Ilam’s Environmental Protection Office, was the most polluted day of 2015, so the amount of aerosol recorded was 1200 µg/m3 in the air of Mehran City. To analyze the storm structure, a combination study was performed using NECP/NCAR reanalyzed digital data and output of dynamic and regional models. The first group consisted of three regional models of NAAPS, DREAM 8b and NMMB/BSC, and the second group included HYSPLIT dynamic model with backward method. NECP / NCAR data are also used in the synoptic analysis of the storm.
The average slope of air pressure in the sea level at the time of the dust storm in the west of Iran has increased and a high pressure difference of 20 hPa is observed between east and west of Iran, which is accompanied by a high pressure difference and severe winds in the southwestern borders of Iran. Also, the surface moisture flux of the soil has fallen sharply for the day of the storm occurrence in the study area. High advection in the Western part of Iran has been accompanied by a change in the density and mass of the air with heat, resulting in very rapid and intense air rotational movements around the Earth's surface; on the other hand, the coincidence of the positive and negative vorticity in a single significant amount in the formation of the lower level jet has caused the emergence of the dust storm to occur in the mentioned day. On the day of the dust storm, the orbital component of the wind speed was Western, and its velocity was more than 5 meters per second on the western borders of the country. The meridian component of the wind speed was also Southern. Therefore, the effect of present pattern on west of Iran during the day of storm dust has played a significant role. The optical depth index and surface dust concentration index in the NAAPS model have shown that dust concentrations ranged from 640 to 1260 µg/m3 to the west. Besides, the amount of sulfate in the region was estimated to be between 1 and 2 µg/m3. Comparison of the output of DREAM Bb and NMMB / BSC models showed an increase in concentration values per Dust surface unit on the day of storm occurrence. Based on the results of two models of DREAM Bb and NMMB / BSC in the case of western dust in Iran, it can be concluded that the effect of local factors and close proximity to the centers of the dust source have a significant role in the occurrence of present phenomena for western Iran. The simulation of the Dust storm direction with the HYSPLIT dynamic model and the backward method has shown two routes of dust entering the west of the country; a) Northwest - Southeast; b) West-East direction. The main origins of the first route, the northwest of Iraq and the east of Syria, and the second route were the center of Iraq.
Keywords: Spring dust storms, Regional modeling, HYSPLIT model, particles optical depth, West Iran
Zahra Mosaffaei, Ali Jahani, Mohammad Ali Zare Chahouki, Hamid Goshtasb Meygoni, Vahid Etemad,
Volume 8, Issue 3 (12-2021)
Volume 8, Issue 3 (12-2021)
Abstract
Risk modeling of plant species diversity and extinction in Sorkheh_hesar National Park
Zahra Mosaffaei1, Ali Jahani2*, 3MohammadAli ZareChahouki, 4Hamid GoshtasbMeygoni, 5Vahid Etemad
1 Masters of Natural Resources Engineering, Environmental Sciences, College of Environment, Karaj
*2Associate Professor, Department of Natural Environment and Biodiversity, College of Environment, Karaj.
3 Professor, Department of Restoration of arid and mountainous regions, University of Tehran, Karaj
4 Associate Professor, Department of Natural Environment and Biodiversity, College of Environment, Karaj
5 Associate Professor, Department of Forestry and Forest Economics, University of Tehran, Karaj
Abstract
Full identification of hazards and prioritizing them for non-harm to nature is one of the first steps in natural resource management. Therefore, introducing a comprehensive system of evaluation, understanding, and evaluation is essential for controlling hazards. This study aimed to model and predict environmental hazards following increased degradation in natural environments by ANN. Thus, 600 soil and vegetation samples were collected from inhomogeneous ecological units. Soil samples were prepared by strip transect method according to soil depth in four profiles (5, 10, 15, 20 cm). Vegetation samples were also collected using a minimum level method using 2 2 square plots according to the type, density, and distribution of vegetation. Sampling was done in two safe zones and other uses were modeled using ANN in MATLAB environment. The optimal model of multilayer perceptron with two hidden layers, sigmoid tangent function and 19 neurons per layer and coefficient of determination of 0.90. The results of sensitivity analysis showed that soil moisture content would be effective in decreasing biodiversity and flood risk as well as increasing the risk of extinction of endemic species in the region, and then the apparent and true gravity and soil porosity and distance from the road play a key role in the degradation of cover. Vegetation has increased flooding and extinction risk. Therefore, it is recommended that measures related to soil and vegetation restoration in this park be taken to reduce future damages as soon as possible.
Keywords: Modeling, Artificial Neural Network, Environmental Hazards, National Park, Vegetation
Dr Javad Mozaffari, Mohamad Pooranvari, Dr Seyed Asadolah Mohseni Movahed,
Volume 10, Issue 1 (5-2023)
Volume 10, Issue 1 (5-2023)
Abstract
Introduction
Soil erosion is the process by which soil particles and components are separated from their main bed by an erosive agent and transported to another location. In the soil erosion process, there are three distinct phases: 1- separation of soil particles, 2- particle transfer and 3- sedimentation of transported materials. In water erosion, the erosive factors are rainfall and runoff. Erosion and the consequent reduction of soil fertility are among the issues that make it difficult to achieve sustainable agricultural development and environmental protection. It is important to study the quantity and quality of erosion in the country's watersheds and to prevent the loss of one of the richest and most valuable natural resources of the country, namely soil, and to fight against this process. (Tabatabai, 1392). Therefore, to calculate the rate of erosion and sediment production in most watersheds of the country that lack statistics or lack of statistics, the use of experimental models to estimate erosion and sediment is required. According to what has been said, the present study was conducted based on the following two main objectives: 1- Estimation of erosion and sediment in Adineh Masjed watershed, which is one of the main sub-basins of Kamal Saleh Dam, using EPM and MPSIAC experimental models and 2- Investigation and comparing two models and choosing a better model for similar regions and climates.
Materials and methods
Adineh Masjid watershed is one of the sub-basins of Dez and the main sub-basin of Kamal Saleh dam. Temperature, isotherm, geology of the area, slope and available information were performed and finally, by interpreting the photos, types, land units, current land use were determined and updated with field control. For a more detailed study, first, according to the condition of the main waterway and changes in the appearance of the land and vegetation and new land material, the ridges separating the basin were divided into 15 sub-basins. In EPM model, four watershed erosion coefficient (Ψ), land use coefficient (Xa), rock and soil susceptibility coefficient to erosion (Y) and average basin slope (I) and in MPSIAC model, nine geological, soil, climate factor (Climate), runoff, slope, vegetation, land use, current erosion status and waterway erosion are examined. Each model was scored according to data analysis and digital images and then placed in the relevant formula. Finally, the amount of erosion and sediment in the basin was estimated and the sedimentation class of the area was determined.
Results
To determine the score of nine factors affecting soil erosion using MPSIAC method and the four factors of EPM model, each of the factors affecting erosion in units were analyzed. Finally, by weighting, the points of each factor in the models were calculated. The degree of R deposition from the sum of the nine factors of MPSIAC model and the degree of Z erosion was obtained by combining the four EPM factors. Then, the amount of sediment production and erosion in the field of relationships related to each model was calculated and compared and analyzed. In MPSIAC model, the amount of specific sediment (M3 / Km2 / year) was calculated as 112.713 and the specific erosion (M3 / Km2 / year) was calculated as 375.71. In the EPM model, the amount of specific sediment (M3 / Km2 / year) was calculated as 213.95 and Specific erosion (M3 / Km2 / year) was calculated to be 395.86.
Discussion and conclusion
The results of sediment and erosion estimation were estimated separately for each sub-area using two models and it was found that the two models are somewhat relatively compatible with each other. The results of MPSIAC model, have more accuracy and reliability, and therefore the results of the MPSIAC model can be used to estimate the amount of sediment entering the Kamal Saleh Dam. However, due to the small distance between the results of the two models, if we do not have access to MPSIAC model data in similar areas, the EPM model can be used with less data and more easily accessible. It was also observed that in the upper and entrance parts of the basin, where the slope is higher and the vegetation is less, the amount of sediment production and erosion is higher in these areas. So that the upper parts of the basin are in the medium erosion class and the rest of the basin is in the low erosion class.
Keywords: watershed, erosion and sediment, modeling
Roshanak Afrakhteh, Abdolrasoul Salman Mahini, Mahdi Motagh, Hamidreza Kamyab,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
This paper is a discussion of urban heat islands (UHIs), which unique residential areas are characterized by dense central cores surrounded by less dense peripheral lands. UHIs experience higher temperatures due to impermeable surfaces and specific land use patterns. These temperature variations have negative environmental and social impacts, leading to increased energy consumption, air pollution, and public health concerns. It emphasizes the need for simpler approaches to comprehend UHI temperature dynamics and explains how urban development patterns contribute to land surface temperature variation. The case study of Guilan Plain illustrates the relationship between development patterns and temperature, utilizing techniques like principal component analysis and generalized additive models.
This paper focuses on mapping land use and land surface temperature in the southwestern region of the Caspian Sea, specifically in the low-lying area of Guilan province. The research utilized satellite data from Landsat sensors for three different time periods: 2002, 2012, and 2021. A spatial unit known as a "city block" was employed through object-based analysis using eCognition software. Thermal bands from Landsat, such as TM band 6, ETM+ band 6, and TIR-1 band 10, were used to retrieve land surface temperature. The radiative transfer equation was used to calculate temperature, accounting for atmospheric and emissivity effects.
The study employed the normalized difference vegetation index (NDVI) method to estimate land surface radiance. The main focus of the study was to identify predictive variables for urban land surface temperature within the context of residential city blocks. These variables were categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Intrinsic variables included block area, shape index, perimeter-to-area ratio, and central core index, calculated using Fragstats software. Neighboring variables encompassed metrics like shared boundary length, mother polygon area, number of neighboring blocks, average distance to neighboring block centers, average area of neighboring blocks, average shape index of neighboring blocks, and average central core index of neighboring blocks. Principal Component Analysis (PCA) was employed to select significant variables that captured the majority of data variance. Variables with eigenvalues greater than 1 in each principal component were considered significant contributors. Varimax rotation was applied to the PCA results to ensure accurate variable selection.
The study utilized a Generalized Additive Model (GAM) approach, implemented using the mgcv package in R, to model the relationship between urban land surface temperature and predictor variables. Smoothing parameters were estimated using a restricted maximum likelihood method. Model accuracy and interpretability were assessed using the coefficient of determination (R-squared) and the F-test analysis. the study's results include the generation of land use maps for three different time periods using object-based image analysis. Urban block characteristics were aligned with spectral units through density, shape, and scale coefficients. Over the years, the average block size showed variation, increasing from 61.19 hectares to 62.21 hectares. Urban expansion was observed across the years, with the urban area expanding from 9.5% to 11.1% of the region. Surface temperatures ranged from 22.84 to 26.26°C, with urban temperatures spanning 26.14 to 53.04°C. Independent variables were calculated for intrinsic and neighboring categories, with varying characteristics like block size, shape index, and perimeter-to-area ratio. Principal Component Analysis identified influential parameters, leading to the selection of block size, and shared boundary. the polygon area, and perimeter-to-area ratio as main variables for a generalized additive regression model. This model demonstrated non-linear relationships between these predictors and urban temperature. Block size, shared boundary, and mother polygon area exhibited a positive relationship with temperature, while the perimeter-to-area ratio displayed a negative trend. The model's performance was satisfactory, indicated by an R-squared value of 0.619.
The discussion focuses on the challenges and complexities of predicting urban surface temperature through studies on land use patterns. the current study concentrates on analyzing surface temperature within urban block units and categorizing variables into intrinsic and neighboring factors to enhance the understanding of the relationship between urban surface temperature and spatial distribution. Despite calculating urban surface temperature as a seasonal average across years, notable variations in temperatures were observed across different years. These variations are attributed to environmental conditions, climatic factors, and atmospheric influences that fluctuate over time. Consequently, the study aims to mitigate the impact of dynamic parameters by basing its models on cumulative temperature changes over various years. However, despite its reliability, this approach might lead to biased results when dealing with short-term time-series imagery.
The discussion also delves into the study's approach of focusing on spatial indices of urban units as predictive neighboring parameters. This choice stems from the fact that other units, particularly agricultural ones, experience significant changes over shorter periods, which can disrupt model calibration. Principal Component Analysis highlights the importance of block size as a key predictor of urban surface temperature, emphasizing the shift from polygon area to block size as a spatial scale. The study concludes that both block size and aggregation significantly influence urban temperature patterns. The Generalized Additive Model reveals that block size and mother polygon area exhibit a positive relationship with urban surface temperature, while the perimeter-to-area ratio displays an inverse correlation. This parameter indicates that units with smaller central cores and higher perimeter-to-area ratios experience cooler temperatures due to engagement with neighboring units, especially agricultural ones. In conclusion, the findings suggest that urban blocks function as distinct entities where temperature-related factors are influenced by intrinsic attributes like shape, as well as by the positioning of a unit relative to others.
The conclusion highlights the continuous growth of studies investigating the connection between land use patterns and urban surface temperature. Block size emerges as a central factor in determining urban surface temperature, alongside block dispersion and aggregation, which play crucial roles as predictors in residential areas. Additionally, the study emphasizes the importance of spatial configuration and unit structure in shaping urban temperature patterns. The proposed methodology has the potential to enhance understanding of parameter significance in shaping urban temperature patterns across various regions of Iran.
This paper focuses on mapping land use and land surface temperature in the southwestern region of the Caspian Sea, specifically in the low-lying area of Guilan province. The research utilized satellite data from Landsat sensors for three different time periods: 2002, 2012, and 2021. A spatial unit known as a "city block" was employed through object-based analysis using eCognition software. Thermal bands from Landsat, such as TM band 6, ETM+ band 6, and TIR-1 band 10, were used to retrieve land surface temperature. The radiative transfer equation was used to calculate temperature, accounting for atmospheric and emissivity effects.
The study employed the normalized difference vegetation index (NDVI) method to estimate land surface radiance. The main focus of the study was to identify predictive variables for urban land surface temperature within the context of residential city blocks. These variables were categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Intrinsic variables included block area, shape index, perimeter-to-area ratio, and central core index, calculated using Fragstats software. Neighboring variables encompassed metrics like shared boundary length, mother polygon area, number of neighboring blocks, average distance to neighboring block centers, average area of neighboring blocks, average shape index of neighboring blocks, and average central core index of neighboring blocks. Principal Component Analysis (PCA) was employed to select significant variables that captured the majority of data variance. Variables with eigenvalues greater than 1 in each principal component were considered significant contributors. Varimax rotation was applied to the PCA results to ensure accurate variable selection.
The study utilized a Generalized Additive Model (GAM) approach, implemented using the mgcv package in R, to model the relationship between urban land surface temperature and predictor variables. Smoothing parameters were estimated using a restricted maximum likelihood method. Model accuracy and interpretability were assessed using the coefficient of determination (R-squared) and the F-test analysis. the study's results include the generation of land use maps for three different time periods using object-based image analysis. Urban block characteristics were aligned with spectral units through density, shape, and scale coefficients. Over the years, the average block size showed variation, increasing from 61.19 hectares to 62.21 hectares. Urban expansion was observed across the years, with the urban area expanding from 9.5% to 11.1% of the region. Surface temperatures ranged from 22.84 to 26.26°C, with urban temperatures spanning 26.14 to 53.04°C. Independent variables were calculated for intrinsic and neighboring categories, with varying characteristics like block size, shape index, and perimeter-to-area ratio. Principal Component Analysis identified influential parameters, leading to the selection of block size, and shared boundary. the polygon area, and perimeter-to-area ratio as main variables for a generalized additive regression model. This model demonstrated non-linear relationships between these predictors and urban temperature. Block size, shared boundary, and mother polygon area exhibited a positive relationship with temperature, while the perimeter-to-area ratio displayed a negative trend. The model's performance was satisfactory, indicated by an R-squared value of 0.619.
The discussion focuses on the challenges and complexities of predicting urban surface temperature through studies on land use patterns. the current study concentrates on analyzing surface temperature within urban block units and categorizing variables into intrinsic and neighboring factors to enhance the understanding of the relationship between urban surface temperature and spatial distribution. Despite calculating urban surface temperature as a seasonal average across years, notable variations in temperatures were observed across different years. These variations are attributed to environmental conditions, climatic factors, and atmospheric influences that fluctuate over time. Consequently, the study aims to mitigate the impact of dynamic parameters by basing its models on cumulative temperature changes over various years. However, despite its reliability, this approach might lead to biased results when dealing with short-term time-series imagery.
The discussion also delves into the study's approach of focusing on spatial indices of urban units as predictive neighboring parameters. This choice stems from the fact that other units, particularly agricultural ones, experience significant changes over shorter periods, which can disrupt model calibration. Principal Component Analysis highlights the importance of block size as a key predictor of urban surface temperature, emphasizing the shift from polygon area to block size as a spatial scale. The study concludes that both block size and aggregation significantly influence urban temperature patterns. The Generalized Additive Model reveals that block size and mother polygon area exhibit a positive relationship with urban surface temperature, while the perimeter-to-area ratio displays an inverse correlation. This parameter indicates that units with smaller central cores and higher perimeter-to-area ratios experience cooler temperatures due to engagement with neighboring units, especially agricultural ones. In conclusion, the findings suggest that urban blocks function as distinct entities where temperature-related factors are influenced by intrinsic attributes like shape, as well as by the positioning of a unit relative to others.
The conclusion highlights the continuous growth of studies investigating the connection between land use patterns and urban surface temperature. Block size emerges as a central factor in determining urban surface temperature, alongside block dispersion and aggregation, which play crucial roles as predictors in residential areas. Additionally, the study emphasizes the importance of spatial configuration and unit structure in shaping urban temperature patterns. The proposed methodology has the potential to enhance understanding of parameter significance in shaping urban temperature patterns across various regions of Iran.
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