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Showing 5 results for Ramezani Etedali
Evaluation of TRMM and ECMWF Data for Drought Time Monitoring (Case Study: Kurdistan Province)
M. Ahmadi, H. Ramezani Etedali, A. Kaviai, A.r. Tavakkoli,
Volume 27, Issue 1 (Spring 2023)
Abstract
Studying the effects of drought in mountainous areas is facing problems due to the inappropriate distribution of stations, the lack of long-term data, and areas lacking statistics. Therefore, the main objective of this research was to investigate the drought indices of Kurdistan province using TRMM satellite data and ECMWF dataset, as well as to evaluate their accuracy against the data of land stations in Kurdistan province. First, ECMWF precipitation data for the 2000-2020 period and TRMM precipitation data for the 2000-2019 period were obtained and evaluated using RMSE, MBE, and correlation coefficient statistics. Spearman's correlation coefficient showed a significant relationship between the TRMM satellite precipitation data and the ECMWF dataset with ground stations at the 5% level, and the value of this coefficient was between 0.95-0.85. According to the results, it can be acknowledged that the TRMM satellite rainfall and ECMWF dataset in the monthly time scale had proper accuracy at the Kurdistan province level. Therefore, these two sources were used to examine the drought indices. SPI, SPEI, and ZSI drought indices were calculated in different monthly periods (1-48), PNI in different monthly, seasonal, and annual periods in Kurdistan province (Saqqez, Qorveh, Bijar, Sanandaj stations). Spearman's correlation coefficient indicated a significant relationship at the 5% level between the SPI, ZSI, PNI, and SPEI index of the ECMWF dataset with ground stations. The results of the SPI index showed that the lowest RMSE value for the TRMM satellite at the Saqqez station and the three months was equal to 0.45, and for the ECMWF dataset at the Sanandaj station and the 24 months was equal to 0.35.

Evaluation of ERA5 Reanalysis Dataset for Simulation of Climatic Variables and Water Harvesting from Humidity (Case Study: Qazvin Province)
S. Koohi, B. Bahmanabadi, Z. Partovi, F. Safari, M. Khajevand Sas, H. Ramezani Etedali, B. Ghiasi,
Volume 27, Issue 4 (Winter 2023)
Abstract
Water supply remains a significant challenge in arid and semi-arid regions, and in addressing this concern, unconventional water sources have gained prominence. Notably, the extraction of water from air humidity, classified as an unconventional water source has seen increased adoption. Diverse techniques have been developed to achieve this goal, with the utilization of mesh networks being particularly prevalent. Consequently, this study assesses the evaluation of the performance of the ERA5 dataset in the simulation of atmospheric variables that influence the ability to assess water harvesting from air humidity (including temperature, wind speed, and water vapor pressure). Also, the possibility of water harvesting from air humidity was investigated in Qazvin Province. The outcomes demonstrated the benefit of incorporating adjustment coefficients in estimating temperature and wind speed using the ERA5 dataset. Based on these findings, the northwestern and southern regions of the province (Kuhin and Takestan) exhibit notable potential during spring and summer for water harvesting from the atmosphere. The peak water harvesting for these stations in the summer is estimated at 10.2 and 9.7 l/day.m2, respectively. Using the ERA5 reanalysis dataset, the annual average potential for water harvesting in the stations was evaluated at 7.9 and 4.6 l/day.m2, respectively. Notably, the minimum water harvesting capacity during the summer season recorded in Qazvin is equal to 3.39 l/day.m2, which can be planned for use in irrigation requirements of green spaces, fields, or gardens.

Investigating the Trend of Temperature Changes and Evaluating the Relationship Between Temperature with Yield and Biomass (Case Study: Forage Maize in Qazvin)
F. Safari, H. Ramezani Etedali, A. Kaviani, L. Khosravi,
Volume 28, Issue 4 (Winter 2024)
Abstract
Climatic factors play an important role in the growth and development of plants and affect agriculture. The tolerance threshold of plants for each of these factors is limited. Any change in these factors can directly and indirectly have significant effects on agricultural production. Meanwhile, temperature stress is one of the most important damaging phenomena that causes many problems for production and yield. In this research, the time of occurrence of temperature stress with a statistical period of 44 years (1980-2023) and the relationship between air temperature with yield and biomass were investigated. According to meteorological data, June, July, and August were known as the hottest months of the year. On the other hand, the most heat waves were observed in July and August in the years 1997, 2014, and 2018, which led to a decrease in the quality of the product or the loss of the plant. According to the model evaluation results, the accuracy of the model in simulating days to flowering and days to maturity was confirmed using R2 (0.8 and 0.51) and NRMSE (15.36 and 7.12). Also, the model was simulated for the studied fields with deviation percentages of 1.92, 5.65, 4.94, 1.58, 0.96, and 1.49%, respectively. It showed that the model had a satisfactory performance and could be used for maize production planning. Next, the relationship between temperature, yield, and biomass was investigated, and there was a negative and significant relationship between temperature, yield, and biomass at the 99% confidence level.

Assessing the Production of Cereals Exposed to Drought Due to Climate Change in Iran
H. Ramezani Etedali, S. Koohi,
Volume 29, Issue 1 (Spring 2025)
Abstract
Agriculture, as a crucial economic and social sector in Iran, has always been significantly influenced by weather conditions, water availability, and farm management practices. Enhancing productivity and optimizing resource management in crop production are essential to achieving sustainable agricultural development and ensuring food security. This research aimed to investigate how much wheat, barley, and corn production, separately from irrigated and rainfed crops, will be affected by the severity of climatic drought (based on the CMIP6) in Iran. This research was carried out using the amount of wheat, barley, and corn production in all the provinces, which was provided by the Agricultural Jihad Organization during the years 1371 to 1402. Climate data was obtained from the NEX-GDDP database, and the De Martonne aridity index was calculated to investigate changes in aridity under climate scenarios. The results indicated that during the baseline period, the production of rainfed wheat, barley, and corn under semi-arid to very arid climatic conditions was approximately 2,076, 434, and 15 thousand tons per year, respectively. With the intensification of arid conditions across the country, these production levels are projected to increase to 3,333, 693, and 16 thousand tons under the SSP2 scenario and further rise to 3,558, 842, and 16 thousand tons under the SSP5 scenario. Additionally, the production of irrigated wheat, barley, and corn in semi-arid to very arid climatic conditions during the baseline period stands at approximately 6,240, 1,683, and 5,842 thousand tons, respectively. Under the SSP2 climate scenario, the production is expected to reach about 7,126, 1,757, and 6,253 thousand tons, while in the SSP5 scenario, the estimated production is approximately 7,348, 1,780, and 6,324 thousand tons. The findings revealed notable spatial differences in crop production across the country, highlighting that the climatic conditions, particularly in the central, southern, southeastern, and southwestern regions, are becoming increasingly arid. It is crucial to implement smart planning and policies, adopt advanced technologies, and improve the management of water and soil resources to mitigate the adverse impacts of these changes and better adapt to evolving conditions. Addressing these challenges and implementing effective measures are essential steps toward achieving sustainability in the agriculture and natural resources sectors.

Wheat Production Simulation Using Sentinel 2 Images and Machine Learning Techniques
H. Ramezani Etedali, M. Ahmadi,
Volume 29, Issue 2 (Summer 2025)
Abstract
change, accurately predicting wheat production is essential for developing precision agriculture. Remote sensing enables the indirect prediction of crop production before harvest. This research investigates the application of the random forest method and support vector regression for simulating wheat production across ten selected farms in Qazvin Plain from 2019 to 2020, employing NDVI, MSAVI, and EVI vegetation indices. Sentinel 2 satellite data was utilized for the vegetation indices. Production data for the ten wheat fields was obtained from the Agricultural Jihad Organization of Qazvin Province. Evaluation of support vector regression and random forest to assess both the observed and simulated wheat production data was conducted using R2, MBE, RMSE, and MAE statistics. To explore the simulation of wheat production using vegetation indices, seven methods were defined: methods 1 to 3 examine each index separately; methods 4 to 6 focus on binary combinations of the indices; and method 7 considers the combined effects of all three indices. The support vector regression model provided good estimates of wheat production in all methods, except methods one and four, in the test phase, with a coefficient of determination of more than 0.98 and a low RMSE. The random forest model showed significant results in all methods except methods two and six during the test phase, achieving a 95% probability (P-value=0.00) with a coefficient of determination greater than 0.8. Overall, this research highlights the importance and potential of machine learning techniques for timely crop production prediction as a strong foundation for regional food security.

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