Showing 5 results for Zarei
Dr. Mostafa Kabolizadeh, Dr. Sajad Zareie, Mr. Mohammad Foroughi Rad,
Volume 0, Issue 0 (3-1921)
Abstract
| There are various indicators to monitor and management of agricultural water resources in arid and semi-arid countries including Iran, some of which can be extracted directly in situ, and some can be retrieved using remote sensing technology and satellite images. Aim of this study is to propose the most appropriate and efficient indicators of agricultural water resource management for achieving maximum production and maximum water efficiency using remote sensing technology, therefore, Crop Water Stress Index (CWSI) and Surface Energy Balance Algorithm (SEBAL) were used to estimate Evapotranspiration (ET). In the first step, ET rate was calculated using SEBAL algorithm for six Landsat 8 satellite images related to the wheat growth period. Then, zoning of this index was done in the range of zero to one, in four categories of very low, low, medium and high, which respectively indicate the lowest to the highest amount of ET. In next step, CWSI was calculated based on Idso equation, and its results show different changes both in cold season and in warm months. Comparison of ET and CWSI shows a significant relationship between these two indices in warm months, while in cold months, no significant relationship can be seen. These findings along with the established relationship between ET and CWSI can inform water management strategies in arid environments for sustainable crop production. |
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Dr Abazar Solgi, Dr Heidar Zarei, , ,
Volume 18, Issue 50 (3-2018)
Abstract
Estimating and predicting precipitation and achieving its runoff play an important role to correct management and exploitation of basins, management of dams and reservoirs, minimizing the flood damages and droughts, and water resource management, so they are considered by hydrologists. The appropriate performance of intelligent models leads researchers to use them for predicting hydrological phenomena more and more. Therefore, in this study, the Gene Expression Programming (GEP) and Support Vector Regression (SVR) models were used to model monthly precipitation of Nahavand City. In this study, precipitation, temperature, and relative humidity data were used in a 32-year period (from 1983 to 2014). The results showed that the same and good performance of both models (R2= 0.92), but according to different evaluation criteria, GEP model showed a little better performance (RMSE= 0.0478 and 0.0486), while the running GEP model is so easier than the SVM model. Totally, it can be said that GEP model had been suitable for modeling monthly precipitation of Varayeneh station in Nahavand City. Finally, the monthly precipitation was predicted the GEP which showed a decrease in precipitation in compared with previous months.
Zahra Hejazizadeh, Sharifeh Zarei, ,
Volume 23, Issue 69 (6-2023)
Abstract
Abstract
In recent years, attention has been paid to climate change, which could be the result of economic, social, and financial losses associated with extreme weather events. The purpose of this study is to investigate the variation of extreme temperature and precipitation in Kurdistan province. For this purpose, daily rainfall data, minimum temperature and maximum temperature of 6 stations were used during the statistical period (1990-1990). And their changes during the period (2041-2060) using the universal HadGEM2 model under two scenarios RCP2.6 and RCP8.5 and the LARS-WG6 statistical downscaling were investigated. In order to study the trend of climatic extreme indexes, rainfall and temperature indices were analyzed using RClimdex software. The results showed that during the period (2016-1990), hot extreme indicators have a positive and incremental trend. This trend is significant for the "number of summer days" and "maximum monthly of maximum daily temperature" indicators. This is while the cold extreme indexes had a decreasing and negative trend. This trend was significant only for the "cold days" index. Extreme precipitation in Kurdistan province has a negative trend in most stations. ،this trend is significant at most stations, that indicates a reduction in the severity, duration and frequency of precipitation during the study period. The results of the climate change outlook also indicate that the temperature will increase over the next period and rainfall will decrease.
Mr Abazar Solgi, Dr Heidar Zarei, Dr Safar Marofi,
Volume 24, Issue 72 (3-2024)
Abstract
Different methods are used for baseflow separation, filtering method one of these methods. In this study, the filtering methods with different algorithms, inclusive One-Parameter, Two-Parameter, Three-Parameter, Lyne & Hollick, Chapman, Furey & Gupta, Eckhardt and Ewma used for daily baseflow separation of Kahman karst spring in Aleshter county. The statistical period used in this study was a period of 27 years. The Isotope content method used as the main method for baseflow separation. Samples analyzed at the Mesbah Energy Company laboratory. Each algorithm has different parameters. First the parameters of each algorithm optimized based on the isotopic content method in the water year of 2017-2018. Then the optimized parameters used for the period 27 years. At the end, using the evaluation of different critical compared to the different algorithms. The results showed that the Eckhart algorithm performs better than other algorithms. This algorithm estimated baseflow and surface water indexes, respectively 81 and 19 percent.
Sharifeh Zarei, Dr. Bohloul Alijani, Dr. Zahra Hejazizadeh, Dr. Bakhtiar Mohammadi,
Volume 25, Issue 78 (9-2025)
Abstract
This study investigates the most significant synoptic patterns associated with widespread snowfall in the eastern half of Iran. To achieve this, weather code data and snow depth records from synoptic stations in the eastern half of the country were obtained from the Iranian Meteorological Organization for the statistical period of 1371-1400 (1992-2021), focusing on the months of October to March. Days with simultaneous snowfall covering more than 70% of the study area were identified as widespread snowfall events. For the synoptic-dynamic analysis of these events, a classification method utilizing cluster analysis was employed. Maps of representative days were generated, including variables such as atmospheric temperature, moisture flux, geopotential height, vorticity, front formation, jet stream location, omega index, and meridional and zonal wind data. Additionally, trend analysis was conducted using the Mann-Kendall test. The results revealed that three primary synoptic patterns are responsible for widespread snowfall in the study area. These patterns include: (1) high-pressure systems over Siberia and central Europe coupled with low-pressure systems over eastern Iran; (2) high-pressure systems over western Iran paired with low-pressure systems over Sudan; and (3) high-pressure systems over central Europe combined with low-pressure systems over eastern Iran and Afghanistan. In all patterns, the intensification of meridional flows in the westerly winds, along with the formation of high- and low-pressure centers, creates blocking conditions that disrupt the westerly flow and promote upward air motion. The concentration of negative omega fields and positive relative vorticity advection, coupled with the positioning of northeastern Iran in the left exit region of the Subtropical Jet Stream, contributes to significant atmospheric instability and widespread snowfall in the region. Furthermore, the trend analysis indicated that, although there is no statistically significant trend in the number of snowfall days in northeastern Iran, the overall number of snowfall days has decreased over time.
