In this study, the dust situation in 6 synoptic stations of Kermanshah province was investigated in the period 1987-1992. To study this phenomenon, dust days were extracted in 2009 with code (06) from the current air index. Then data from different bar levels were taken from the National Oceanic and Atmospheric Organization (NCEP / NCAR) database and maps were plotted in the Grads software. Finally, through the processing of MODIS satellite imagery, with the application of the brightness temperature index, round detection and the dust was made and its territory expanded. The results of the study showed that at low ground level, when the European high-pressure system retreated to the north and west, Sudan's low pressure existed in the eastern Mediterranean, and the system moved southwest to the northeast, and when they entered, the disturbance zone is caused due to the lack of moisture in the dust, the main sources of dust in the province of Kermanshah include the deserts of northern Arabia, southern Iraq, and somewhat north of the sub-Saharan Africa.

In this study, we tried to identify the sources of moisture and its direction of heavy rainfall in south and southwest of Iran by using a new algorithm based on atmospheric rivers. For this purpose, daily rainfall of 17 synoptic stations in the period 1986 to 2015 in south and southwestern Iran that have a common time span and fully cover the study area is used.Also from the data set of the National Center for Environmental Prediction / National Center for Atmospheric Research (NCEP / NCAR) European Mid-Term Forecast Center (ECMWF) and ERA-interim data with spatial resolution of 0.75 It was used at 0.75 latitude and longitude with 6 hour resolution. The variables used are integrated water vapor (IWV), specific humidity (q), and orbital and meridional components (u, v). In this research, an algorithm based on the calculation of Vertical Horizontal Vapor Transfer Integral (IVT) is used to identify and navigate atmospheric rivers. The results show that the main source of rainfall moisture is in south and southwestern Iran, south of Red Sea and Gulf of Aden. Of course, the maps show that the Arabian Sea was not affected by the humidity.The Arabian Peninsula also, due to the high moisture transfer rate, as a transitional route, transmits a large amount of moisture to the study area.Finally, the path of moisture to the study area was mapped and identified, and thus considering the three main conditions for the atmospheric river, it can be said that the path obtained is the same as the atmospheric river.

Rain water harvesting is an appropriate option for storing surface runoff for subsequent uses during periods with limited access to water. The most important step in the application of rainwater harvesting systems (RWH) is the site selection suitable areas. Therefore, by identifying suitable sites for this purpose, time and cost will be saved . In this research, multivariate regression model and GIS were used to site selection in situ (RWH) in Tajare watershed. For this purpose, layers such as crown cover, litter, rock and stones, soil, curve number, rainfall, slope and depth of field as independent variable and infiltration were considered as the dependent variable. Then, according to the maps, their values were calculated in average for each of the 27 sub-basins. Also, to investigate the relationship between these variables and weighting, each of the effective layers of multi-variable regression was used by the stepwise method The results showed that the linear multivariate regression model with an explanation coefficient of 0.993 was able to estimate the penetration factor values well In terms of grade of importance, the curve number variables with a coefficient of -2.433, depth of soil with a coefficient of 0.3488, and rubble and gravel percent with a coefficient of 0.057, were the most important, and other factors were not significant. Comparison of the map from the site selection of multivariate regression in this research with some recommended criteria of various research studies showed that the predicted classes with good in the central parts of the basin and very good in the upstream areas of the basin which in the eastern and southeastern part of the basin fit have a good overlap with the recommended areas with these criteria.

Climate change is a significant environmental concern due to the sensitivity of glacier melting processes and snow density to climate variations. Currently, a range of satellite sensors, including AVHRR, MODIS, GEOS, and MERIS, are employed for snow monitoring and are extensively utilized to analyze fluctuations and changes in global snow cover. The MODIS sensor is particularly favored for its extensive global spatial coverage, suitable spatial accuracy, and frequent temporal coverage across various scales. Consequently, this study utilizes snow products derived from the MODIS sensor. In this investigation, data on snow-related days from the statistical period of 1989 to 2018 were collected for three provinces: Kermanshah, Ilam, and Lorestan. This data was processed using MODIS snow cover information pertinent to the Middle Zagros region, in conjunction with remote sensing techniques. The study provides a detailed examination of snow cover changes within the specified area. The Normalized Difference Snow Index (NDSI) was employed to detect snow cover within the MODIS sensor products. To differentiate pixels and identify various phenomena, the acquired images were processed using Geographic Information Systems (GIS) technology. Analysis of seasonal snow cover changes using MODIS sensor imagery indicates a significant decreasing trend in the majority of the studied area, particularly in the elevated regions. Notably, only the western and southwestern regions of the study area exhibit no discernible decreasing trend. Furthermore, the examination of snow-covered days throughout the study period reveals a decrease in snow cover in the Middle Zagros, with these changes intensifying in recent years, especially in the snow-covered areas of the region. Changes in snow cover during the winter months, particularly in elevated areas, were observed to be more pronounced compared to other seasons and regions within the study area.