Moslem Seydi, Kamal Omidvar, Gholamali Mozafari, Ahmad Mazidi,
Volume 25, Issue 77 (6-2025)
Abstract
Abstract
Climate change is an important environmental issue because the melting processes of glaciers and snow density are sensitive to climate change. Today, a variety of satellite sensors such as AVHRR, MODIS, GEOS, MERIS are available for snow monitoring and are widely used to investigate and investigate the fluctuations and changes in snow cover globally. Modis sensor has been considered more because of its global spatial coverage with suitable spatial accuracy and frequent temporal coverage on different scales , Therefore, in the present study, snow products of this sensor were used. In this study, after collecting statistics and data on snow-related days during the statistical period (1989-2018) in three provinces of Kermanshah, Ilam and Lorestan, they were processed using Modis snow cover data in middle Zagros as well as remote sensing techniques, Finally, the snow cover changes in the study area were studied in detail. NDSI index was used in MODIS sensor products to detect snow cover. Consequently, in order to differentiate pixels and identify different phenomena, the received images were processed in GIS environment. . Investigation of snow cover changes in different seasons using Modis sensor images shows that most of the studied area has a significant decreasing trend, especially in the elevated areas of the study area And only in the western and southwestern regions of the study area, there is no specific decreasing trend. Also, the study of snow covered days during the study period indicates a decrease in middle Zagros snow cover and these changes have been intensified in recent years, especially in snow-covered areas of the region. Also, changes in winter and snow-capped and elevated areas were more and more severe than other seasons and other regions in the study area.
Hassan Heidari, Ebrahim Mesgari,
Volume 25, Issue 77 (6-2025)
Abstract
Understanding the daily weather types of any place location plays an important role in identifying its long-term climate. In this study, we employed the Wos classification method, along with a comprehensive climatology approach, to examine key variables such as minimum, average, and maximum temperatures, as well as cloud cover and daily precipitation. Data spanning from 1985 to 2021 were collected from 39 synoptic stations that had a good distribution in the country and had complete statistics, and weather types were identified using coding techniques. The findings revealed that the predominant temperature types in the country are predominantly hot and very hot, with sub-codes indicating generally low to moderate cloud cover and no precipitation. Moreover, the utilization of Ward's clustering method enabled us to identify three distinct climatic groups. The geographical characteristics of each place, such as location, altitude, latitude, proximity to the sea, and synoptic characteristics based on their influence, have the most important effect on the regional separation of groups in the country. The results of this research can be used to determine the weather calendar of each region during different time periods in many fields of agriculture, tourism, etc.
