Showing 3 results for Omidvar
Kamal Omidvar, Mehdi Mahmodabadi, Parisa Shams, Mahbobeh Amiri Esfandegheh,
Volume 7, Issue 3 (11-2020)
Abstract
Due to the fact that the mechanism of anticyclone Movements is the desire to descend and suppress the air, so the effect of these movements and their location in the occurrence of flood falls is significant. For this purpose, in this paper, flood precipitation in the last two decades of Kerman province was studied and two of the most severe ones were selected. Due to the emphasis of this paper on the province of Kerman, the heavy rainfall was calculated for each station in the province using the Gumble Type 1 Distribution Statistical Index. Then, the thermodynamic properties of the precipitation were analyzed using radial data and Kerman station's sketch diagram. For analysis of these floods, daily rainfall data of the synoptic station 10 of the province and sea surface pressure maps and850,500,300 hectopascal levels were used. Then, the arrangement of the simulated pattern and its trend in the air maps, were studied during a selective period daily3. The results of the study indicate that the main cause of flood precipitation in the study area is to strengthen the eastern Mediterranean landfall in the middle troposphere, so when moved downward to the bottom of the polar system, it is transmitted to lower latitudes As a result, Western systems, with their movements on the southern warm waters, have a high moisture content and cause heavy rainfall in the region. Also noteworthy in the occurrence of precipitation is the presence of intense swinging movements on the southern waters, especially the Oman Sea, which causes more humidity to be injected into the interior areas of the country and provides the conditions necessary for the occurrence of such rainfall.
Mrs Shokoufeh Omidi Ghaleh Mohammadi, Dr Ahmad Mazidi*, Dr Kamal Omidvar,
Volume 8, Issue 4 (1-2021)
Abstract
Objective: Chaharmahal and Bakhtiari Province, due to its mountainous location and exposure to Mediterranean and Sudanese synoptic systems, has experienced intense rainfall events and considerable hydrological fluctuations in recent years. These conditions have often led to flash floods and posed serious threats to regional water resources. Accordingly, this study aimed to analyze rainfall intensities, estimate their values for different return periods, and construct Intensity–Duration–Frequency (IDF) curves as well as spatial distribution maps for four synoptic stations: Kouhrang, Farsan, Shahr-e-Kord, and Borujen.
Methods: Precipitation data over a 20-year period (2000–2020) were collected, and rainfall intensities were calculated for durations ranging from 15 to 1440 minutes. Maximum rainfall intensities corresponding to return periods of 2, 5, 10, 25, 50, 100, and 200 years were then estimated using several statistical distributions, including Gumbel, Normal, Pearson type V, and Weibull. Goodness-of-fit tests were applied to identify the most suitable distribution. In addition, spatial interpolation methods within a GIS environment were employed to illustrate spatial patterns of rainfall intensity across the province.
Findings: Results indicated that the Gumbel distribution provided the best fit to the observed data. It was also revealed that rainfall intensity decreases with increasing duration, while it increases with longer return periods. Spatial analyses showed that the highest intensities occur in the northwestern mountainous areas, particularly at Kouhrang station, and gradually decrease toward the southern and eastern parts of the province.
Conclusion: The findings confirm that statistical distributions—particularly the Gumbel model—enable accurate modeling of extreme rainfall events in Chaharmahal and Bakhtiari Province. Moreover, the spatial variability of rainfall intensity highlights the necessity of incorporating such patterns into hydrological infrastructure design, flood management, and water resource planning.
Keywords: Intensity–Duration–Frequency (IDF), Convective Rainfall, IDF Curves, Spatial Distribution, Chaharmahal and Bakhtiari
Email Kamal Omidvar, Email Rohollah Yousefi Ramandi, Email Hajar Toofani,
Volume 11, Issue 3 (12-2024)
Abstract
Air pollution can have serious negative effects on human health, including cardiovascular and respiratory diseases. Monitoring and controlling air pollutants is very important to protect public health and the environment. Like many developing countries, Iran is facing air pollution, especially in its big cities and industrial cities. One of the powerful tools in air pollution monitoring is remote sensing methods. The aim of this study is to use relatively high-resolution satellite data to monitor air quality and air pollution using Sentinel-5 (Sentinel-5P) sensor images. In this study, a comprehensive monitoring based on the values of some of the most important air pollutants (including AI, O3, NO2, SO2, CH4 and CO) has been done using Sentinel-5 satellite images for Iran in 2019-2023. The results of this research showed that the emission of carbon monoxide and sulfur dioxide gases had a decreasing trend (in the months of June as an example of the examined month), but nitrogen dioxide gas, methane gas, ozone gas and aerosols had an increasing trend during the month. from June 2021 to 2023. In general, air pollution is more serious in the northern parts of the country, especially in big cities and several large urban gatherings. In this study, it was investigated how the levels of six air pollutants in Iran vary and differ from June 2019 to 2023. Another important result of this research is that the total amount of air pollution in 2020-2023 has faced an increasing trend compared to 2019. Also, the monitoring by Sentinel-5 satellite images shows that in recent years, Tehran has had the most polluted air in terms of carbon monoxide, nitrogen dioxide, sulfur dioxide and suspended particles (dust). Also, changes in the concentration of pollutants do not follow a specific pattern. It was also found that the GEE system is able to process a large amount of data in a very short time with high accuracy.
