Showing 107 results for Analysis
Arefe Shabani Eraghi, Seyed Mohammad Zamanzade, Fariba Karami,
Volume 25, Issue 79 (12-2025)
Abstract
Reconstructing paleoclimate, particularly environmental temperature, plays a crucial role in understanding both current and future climate patterns. The aim of this research is to investigate the climatic conditions and estimate the ambient temperature during the Holocene period based on two sediment cores extracted from the Jazmurian Basin. Paleotemperature reconstruction was conducted using several methods, including the calculation of the standardized coefficient of variation of oxygen-18 and carbon-13 isotopes. For this purpose, the isotopic analysis of oxygen-18/oxygen-16 and carbon-13 was performed. In Jazmurian core 1, the initial temperature was estimated at 46°C. A decreasing trend of approximately 10°C was observed down to a depth of 175 cm, distributed across eight stratigraphic levels. At 175 cm, the temperature shows an increasing trend, followed by a decline at the subsequent level, and then a return to an increasing and stable trend in the next two levels. In Jazmurian core 2, the initial temperature was approximately 50°C. A sharp decrease in temperature is observed between depths of 80 to 125 cm. Subsequently, there is a slight increase of about 1°C, which remains relatively stable until a depth of 170 cm. Beyond this point, the temperature decreases again in the final two layers. The concentration of carbon-13 in core 1 ranges from 0 to 25.6, while in core 2 it varies between 25.9 and 27.1. In core 1, six carbon -13 isotope samples show a value of zero, indicating an absence of carbon-13 in those sediment layers. In contrast, core 2 displays a narrower range of variation in carbon-13 values. The isotopic and temperature variations observed in these sediment cores reflect different climatic phases during the Holocene in the Jazmurian region. Such climatic changes are often linked to cultural shifts, and the decline of ancient civilizations has frequently coincided with environmental transformations. The findings of this research may be of significant value to archaeology researchers, particularly those studying ancient Iranian civilizations.
Sayyed Mohammad Hosseini,
Volume 26, Issue 80 (3-2026)
Abstract
for the spatial analysis of precipitation in the Middle East, have been used gridded precipitation data from the World Precipitation Climatology Center (GPCC) with a monthly temporal resolution and a spatial resolution of 0.5×0.5 arc degrees. Therefore, a matrix of 80 x 160 dimensions was obtained for the Middle East region (160 longitudinal cells and 80 transverse cells). The reason for choosing network data is their proper spatial and temporal separation and their up-to-date compared to station data. The period under investigation is from 1970 to 2020 AD. Finally, the long-term maps of the Middle East precipitation were drawn on an annual and monthly basis. The results indicate that precipitation in the Middle East tends to concentrate and cluster in the spatial and temporal dimension. In other words, due to the special geographical location of the Middle East region, such as uneven topography, distance and proximity to moisture-feeding sources (Caspian Sea, Black Sea, Mediterranean Sea, Atlantic Ocean, and Indian Ocean) and the direction of unevenness, Precipitation in high altitude areas, It is concentrated in the neighborhood of seas and oceans and also in the windy slopes of the mountain range of the region. The uneven distribution of geographical conditions has caused uneven distribution of Precipitation in the Middle East. So that; The center and gravity of the Middle Eastern Precipitation is concentrated in the eastern end of the Black Sea, southern Turkey in the neighborhood of Syria and Iraq, the Ararat-Zagors belt in the west of Iran, the southern shore of the Caspian Sea, the Pamir highlands and the Bay of Bengal in India, and the Hindu Kush highlands in Pakistan. Is. However, the many parts of the Middle East, due to their proximity to large deserts (African Sahara, Lut Desert, Dasht-Kavir, Arabia's Rab-al-Khali and Afghan deserts), have less than 100 mm of Precipitation. The results showed that the maximum Precipitation of this region has been transferred to the winter season, and the summer season is still the driest period in the Middle East, and only the coasts of the Indian Ocean and the Bay of Bengal have monsoon rains
Zahra Hedjazizadeh, Al Karbalaee, Mokhtar Fatahian,
Volume 26, Issue 80 (3-2026)
Abstract
This study investigates the spatial dynamics of the subtropical anticyclone over Iran during boreal summer, using daily ERA5 reanalysis data (1980–2020) and the Getis-Ord Gi* statistic to identify statistically significant hotspots (p < 0.01) in 500-hPa geopotential height (Z500) anomalies for June–August. Results reveal that the peak statistical hotspot occurs in July: a prominent warm cluster with Z-scores up to +4.1 (99% confidence level) forms over southwestern Iran (27°–32°N, 48°–60°E), reflecting the strongest positive departure from the long-term Z500 climatology. Conversely, a cold cluster with Z-scores reaching −10.2 emerges over the northwest (West Azerbaijan and Kurdistan provinces) the lowest value recorded over the entire period indicating pronounced geopotential depression driven by the orographic influence of the Alborz–Zagros ranges and incursions of mid-latitude systems. Histogram analysis of Z-scores confirms a distinctly bimodal distribution in July, with high frequencies in the [+2.5, +4.1] and [−10.2, −2.5] ranges and a pronounced trough near Z ≈ 0, underscoring strong spatial segregation between warm and cold clusters. Notably, the eastern half of Iran (central and eastern regions) consistently lacks significant hotspots across all three months, suggesting the presence of a dynamic transition zone shaped by the competition between subtropical and mid-latitude circulations. In August, although absolute Z500 exceeds 5890 m, the Z-score diminishes (+4.0), indicating that cumulative surface heating elevates the mean geopotential height but its anomalous intensity relative to climatology weakens compared to July. Collectively, these findings suggest that the dynamical peak of the Iranian subtropical high lags the peak of surface heating by approximately one month.
Mr. Ayat Jahanbani, Mr. Ali Shamie, Mr. Habib-O-Llah Fasihi, Mr. Taher Parizadi,
Volume 26, Issue 81 (6-2026)
Abstract
Resiliency is one of the approaches to reducing the vulnerability of communities and strengthening peoplechr('39')s ability to deal with the dangers of natural disasters, especially earthquakes, and has economic, social, institutional, physical, and environmental dimensions. This research is applied in terms of purpose and descriptive-analytical in terms of nature and research method. The researcher-made questionnaire with 102 items was a tool for collecting research data. The sample size was 386 simple based on Cochranchr('39')s formulas and the sampling method was random. Exploratory factor analysis and path analysis were used in the SPSS25 software platform for data analysis and factor modeling. The results indicate that Parsabad city has the lowest scores in terms of social and physical resilience and is in a moderate to good condition; environmental resilience is in a moderate condition, institutional and economic resilience are in a bad situation. Also question factorization, 13 factors for social dimensions, (behavior during the crisis, crisis awareness, crisis preparedness, knowledge, cooperation, trust, assistance, reliance, interaction, accuracy, attitude, first aid, and necessary measures); 3 factors (Damages, Compensation and ability to return) for economic dimensions; 5 factors (performance of public institutions, the performance of semi-public institutions, institutional communication, institutional measures, and institutional context) for institutional resilience; 4 factors (open space, building resistance, public access and Relief access) for physical resilience and 3 factors (environmental, nutritional and soil factors) for environmental resilience. Finally, the modeling of resilience indicators for Parsabad city was presented.
Shahram Emamgholi, Gholamrezaa Janbaz Ghobadi, Parviz Rezaei, Sadroddin Motevali,
Volume 26, Issue 81 (6-2026)
Abstract
Temperature is one of the basic elements of climate, so its sudden or short-term and long-term changes can change the climate structure of any place. Intense heat waves are one of the most important climatic disasters that have far-reaching effects on various human activities and when they are of high intensity and frequency, they can produce major problems. In this study, to investigate the trend of 49-year frequency series (1970-1970) of hot wave events in Tehran, from two indices of hot days and hot waves (hot days lasting 2 days or more), non-parametric statistics of Sens trend analysis were used. All stations indicate an increasing trend both in the number of hot days in Tehran and in the frequency of hot wave events in 5 stations in Tehran. In this study, two hot waves were identified in Tehran, the first wave in 2010 covered a large part of the central and western parts of the country and the second wave in 2013, which was in all stations of Tehran and even many provinces. Are registered in the country. The results of spatial analysis of hot wave temperature in the statistical blocks of Tehran showed that generally the central areas of Tehran, including areas 6, 7, 8, 10, 11, 12, 14, 15, 4, 7, and 19 significantly It has been affected by the critical temperatures caused by the warm wave rule, while the northern parts of Tehran have been affected by the lower intensities of the hot wave.
Hossein Asakereh, Mansureh Taheri,
Volume 26, Issue 81 (6-2026)
Abstract
One of the climatic characteristics of temperature is the occurrence of extreme temperature. In the present study, the trend of hot days with extreme temperature associated with the coastal plains of the Persian Gulf was investigated. Two environmental and atmospheric databases were used. Environmental data include the average of daily maximum temperature reported from 12 synoptic stations in Persian Gulf coastline (Khuzestan, Bushehr, and Bandar Abbas Provinces) from 1961 to the end of 2018. The extreme temperature for each day temperature was defined to be higher than the average of 75th percentile of the observations at each station and on the same day. Also, the ‘day with extreme temperature’ was applied to a day when the extreme temperature occurred in at least 50% of the stations. The number of hot days with extreme temperature in the study is 554 days, of which 291 days occurred in the warm season and 263 days in the cold season. These days were classified into six groups by performing cluster analysis on sea-level pressure in hot days. Then, for each group, the trend of hot days was examined. In general, it can be concluded that the slope of the line in all groups except the fourth and sixth groups were positive and, as a result, hot days with extreme temperature were increasing.
Dr Saeedeh Fakhari,
Volume 26, Issue 81 (6-2026)
Abstract
Tehran’s District 12, as one of the capital’s cultural and tourism hubs, hosts a collection of prominent cultural institutions and museums that serve as major attractions for domestic and international visitors. However, the absence of systematic planning for routing between these centers leads to wasted time and energy for tourists and diminishes the quality of their visitation experience. This study aims to optimize museum visitation routes in Tehran’s District 12, focusing on minimizing travel time and distance, by selecting 22 active and significant museums in the area as case studies. To achieve this, the mathematical model of the Open Traveling Salesman Problem (Open TSP) was applied within the framework of network analysis in a Geographic Information System (GIS) environment. Precise spatial data—including the geographic locations of museums and the local street network—were imported into ArcGIS software and processed using the Network Analyst tool. Travel cost matrices (based on time and distance) between all museum pairs were calculated, and optimal visitation routes were extracted and ranked using heuristic Open TSP algorithms according to the criteria of minimum time and shortest distance. Findings indicate that applying the Open TSP model within network analysis leads to the identification of significantly more efficient routes compared to conventional patterns or unplanned visits. Quantitative results show that, under normal (non-optimized) conditions, visiting all 22 museums covers a distance of 25.91 km with a travel time of 310 minutes, whereas the optimized proposed route requires only 9.896 km and 118 minutes of travel time. This improvement represents a 62% reduction in both distance and travel time. The study demonstrates the high efficiency of integrating combinatorial optimization models with GIS spatial analysis capabilities for urban tourism planning and can serve as a model for intelligent management of tourist visitation routes in other urban areas. The results enable informed decision-making and optimal planning for both group and individual visits, significantly enhancing the tourism experience by reducing time and physical costs.
