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Showing 44 results for Type of Study: Applicable
Enayat Asdalahi, Mehry Akbary, Zahra Hejazizadeh,
Volume 12, Issue 2 (9-2025)
Volume 12, Issue 2 (9-2025)
Abstract
Objective: The main goal of this research is to identify and analyze the seasonality of the most widespread Torrential rains in Iran during the years 1940 to 2023.
Methods: To achieve this goal, precipitation data was obtained from the ECMWF database with a spatial resolution of 0.25 by 0.25 degrees of arc for the Iranian region during the study period. The next step was to calculate the threshold of torrential precipitation for each cell seasonally using the 95th percentile, and days with torrential precipitation were identified. By applying the condition of the highest spatial spread of the 95th percentile, the days with the most widespread precipitation above the threshold were identified for each season. Finally, the prevailing atmospheric conditions were examined.
Results: Analysis shows that the highest precipitation of 146.85 mm occurs in winter and the lowest of 85 mm occurs in summer. The highest spatial coverage of total precipitation occurs in spring (41.9), winter (40.69), autumn (32.55) and summer (16.84), respectively.The analysis of sea level pressure indicates that during widespread precipitation in the summer, a low-pressure belt extended from the westernmost to the easternmost regions of the upper atmosphere map, encompassing Iran. In contrast, during other seasons, a high-pressure belt was present in the same area. At the 500 hectopascal level in summer, a closed high-pressure dynamic cell was observed over Iran, while at the 850 hectopascal level, two low-pressure centers over Saudi Arabia and Pakistan intensified instability over Iran. Consequently, it is evident that at lower levels, the conditions for atmospheric precipitation were stable, and even the omega level at 500 hectopascals over Iran on that day indicated a weak upward movement of air. However, in other seasons, a trough consistently positioned over western Iran, with active band patterns in spring and winter, facilitated the slowing and diversion of currents toward moisture sources, thereby enabling the transfer of more moisture than normal conditions to Iran. The precipitation study revealed that, except for the summer season, wind dominated over Iran. The presence of wind intensified instability at lower levels. A study of the Atmospheric River reveals that during widespread rainfall across all seasons, the Atmospheric River, which originates from the Red Sea and the Persian Gulf, has consistently been present. However, in the fall and winter seasons, a branch from the Mediterranean Sea also contributes, resulting in increased rainfall.
Dr Bromand Salahi, Mr Mahdi Frotan,
Volume 12, Issue 3 (12-2025)
Volume 12, Issue 3 (12-2025)
Abstract
The ENSO weather phenomenon, including El Niño and La Niña phases, has significant effects on precipitation, temperature, and drought patterns in different regions of the world. This study aimed to investigate the relationship between ENSO indices such as MEI, SOI, and NINO oscillations with drought indices (TCI, VCI, VHI, and SPI) in the provinces of Guilan, Golestan, and Mazandaran from 2013 to 2022. Satellite data of NDVI, LST, and precipitation were extracted from Google Earth Engine to calculate drought indices, and ENSO data were obtained from the NOAA website for correlation analysis. The results showed that the MEI index had a positive and significant correlation with SPI and showed a decrease in drought with an increase in precipitation, but had a weak relationship with other drought indices. The SOI index showed a negative and significant correlation with SPI, indicating the effect of La Niña on increasing drought, especially in Golestan province. The El Niño indices were positively correlated with SPI in the northern provinces of Iran, confirming the effect of reducing drought and increasing precipitation. During the El Niño phase, the northern regions of the studied provinces experienced an increase in temperature and the south of Mazandaran province experienced subzero temperatures, while during the La Niña phase, the temperature increased and the northern regions of the studied provinces experienced higher temperatures. Vegetation was denser in the south of Golestan province and the east of Mazandaran province during the El Niño phase, but it decreased during the La Niña phase. The SPI index showed that drought during the El Niño phase was more widespread and severe in the western half of the studied provinces and more extensive and severe during the La Niña phase. The VHI index showed better vegetation health in El Niño, especially in Gilan and Mazandaran provinces, and decreased health in La Niña phase, especially in Mazandaran and Golestan provinces.
Rana Norouzi, Sayyd Morovat Eftekhari, Ali Ahmadabadi,
Volume 12, Issue 4 (12-2025)
Volume 12, Issue 4 (12-2025)
Abstract
Objective: Over the past two decades, land subsidence has emerged as a significant geomorphological hazard and one of the most critical environmental crises in Iran, causing irreversible damage to many plains each year. Among its primary current causes is the excessive and unregulated extraction of groundwater. The Eshtehard Plain, recognized as one of the industrial and agricultural hubs of Alborz province, is no exception. Due to severe groundwater depletion, it has been officially declared a critical zone by the Ministry of Energy. The objective of this study is to model the risk of land subsidence in this plain using the Random Forest algorithm and to analyze the contributing factors influencing its occurrence
Methods: In this study, twelve independent spatial layers were utilized, including: digital elevation model (DEM), distance to rivers, distance to qanats, distance to wells, distance to faults, groundwater depth, drainage density, soil type, lithology, land use, topographic wetness index (TWI), and solar radiation. The dependent layer consisted of subsidence zones. The Random Forest model was implemented in the R software environment. Two key importance measures—Mean Decrease Accuracy and Mean Decrease Gini—were employed to rank, assess the significance of, and assign weights to the contributing factors of land subsidence. Finally, model performance was evaluated using three complementary metrics: Accuracy, Kappa, and AUCResults: The results demonstrated that the Random Forest model achieved high accuracy in classifying land subsidence risk. Model evaluation showed strong performance with an overall accuracy of 0.963, a Kappa coefficient of 0.611, and an AUC value of 0.955, indicating that the model is highly effective for spatial risk zoning of land subsidence. The most influential variables in subsidence occurrence were identified as groundwater depth, distance to wells, geology, and land use. Furthermore, more than 65% of the study area was categorized as high-risk and very high-risk, reflecting the critical condition of the Eshtehard Plain. Notably, the share of urban land use has shown a steady increase from 2011 to 2023, with a significant spike in 2023, where increased population concentration has placed additional pressure on groundwater resources, leading to an intensification of subsidence in affected areas
Conclusions: The Random Forest algorithm successfully modeled the spatial distribution of land subsidence risk with high accuracy. This method can serve as an effective tool for informed decision-making in groundwater resource management, sustainable development planning, and hazard mitigation in similar regions.
Mrs Shokoufeh Omidi Ghaleh Mohammadi, Dr Ahmad Mazidi*, Dr Kamal Omidvar,
Volume 13, Issue 1 (7-2026)
Volume 13, Issue 1 (7-2026)
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
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