نشریه پژوهشهای تغییرات آب و هوایی
پیاپی 6 (تابستان 1400)

In this study 31 stations were selected according to climatic parameters such as temperature and precipitation. Climatic parameters such as rainfall Monthly and 5 temperature parameters during plant growth from October to June, for 25 years of statistical period from 1982 to 2006, as well as data on wheat yield in each station, a test run test for Data was randomized and then the reconstruction test was performed on missing and missing data. In each station, the correlation coefficient between wheat yield and climatic parameters was calculated and ultimately the model Regression for stations was used to identify effective climatic parameters in estimating wheat yield. In wheat, all stations except Gorgan station had a regression model. In wheat dryland, stations of Urmia, Tehran, Sari, Yasuj, Semnan, Khorramabad had no regression model. The regional model was also calculated for the country and compared with the station models. The yield of blue wheat showed the results of the regression model. In the eastern, central, northeastern and eastern parts of the southern part of the country, the water will fall by 20-35% reduction. In the wheat field discussion, all stations in the northwest are between 30-37%, parts of southwest and south of the country will have a yield reduction of 28-35% And the southeast and north-east half of the 47-42 will increase performance. The highest expected yield in blue wheat For Isfahan station and in rainfed wheat, the most expected performance will be for Zahedan station.

In this study, desertification status of Shadegan town was investigated using IMDPA model, in which statistical analysis and Iranian Model of Desertification Potential Assessment (IMDPA) model were analyzed. Among the 9 standard IMDPA models for the study, two criteria of vegetation and climate, according to the selected area and for every criterion, different indicators were considered In this model, numerical values of criteria were calculated by Geometrical average mean of indices. Finally, desertification of total studied region was estimated from the Geometrical average mean of criteria and final region desertification map was provided using ArcGIS software. The results showed that vegetation criteria with the numerical value 2.39 have the greater effect to criteria climate with the numerical value 1.7 on desertification in Shadegan region. Finally, based on the two investigated criteria, quantitative value of desertification intensity was estimated to be 2.02. Based on the scoring tables of studied model, the region desertification was determined to be moderate.

The purpose of this study is to investigate the patterns of temperature subsidence of surface layers in the days associated with the occurrence of extremely warm temperatures in Iran. To do this, the Asfzari database was used which was prepared based on the mean daily temperature data of 663 synoptic and climatic stations of the country from begging from March 21st, 1961 ending on Jan 1st, 2004. Temperature data, meridional wind component and orbital component, geopotential height, and sea level pressure were also provided in the same time period at 2.5 ° intersections from the reconstructed data set of the NCEP / NCAR database. First, the days with extreme warm temperatures were determined using the normalized temperature deviation index (NTD). Then, based on the magnitude and area of the events, the data were sorted and the first 264 days, when the normalized deviation index was more than 2 ° C (), were selected as the sample of the warmest and most pervasive days. Advection patterns were calculated and plotted at 3 levels of 1000, 925, and 850 hPa along with normalized temperature deviation maps of Iran during the study period. The study of the advection map pattern in all 3 levels showed warm south and west advection towards Iran. After surveys on the geopotential altitude of 500 hPa, sea level pressure values, temperature subsidence maps, and temperature anomaly maps of Iran in the warm days of extremely warm temperatures, it was found that the country is located below the eastern half of the river and below the upper vorticity axis. High pressure on the ground of Iran and low pressure on the ground of western and southern countries has led to the establishment of southern and western currents and the occurrence of these extreme warm temperatures phenomena.

Climate change and global warming are occurring as the most important environmental problem. It is necessary to study the trend of meteorological variables in order to detect climate change in each region. In this study, the trends of variables related to temperature (minimum, absolute minimum, maximum, absolute maximum and average) and precipitation (total precipitation, frequency of days with precipitation and maximum daily precipitation) in the annual time scale were investigated. Trend analysis was performed using non-parametric Mann-Kendall and Sen's slope estimator in 15 synoptic stations of Hormozgan, Bushehr and Sistan and Baluchestan provinces, which had complete data in the period of 1987-2019. Then, zoning maps of climate variables trends were prepared in Arc GIS software. The results showed that 3 temperature variables including: minimum, maximum and average had a significant upward trend in most of the area. The frequency of occurrence of significant upward trend related to temperature variables for each station showed that the stations of Bandar Abbas, Kish Island, Zahedan, Zabol and Iranshahr had a significant upward trend in 4 of the 5 temperature trends studied. Total rainfall in the region showed no significant trend, maximum 24-hour rainfall had a significant increasing trend only in Bushehr-Coastal Station and the frequency of days with rainfall in Bushehr-coastal had a significant decreasing trend. The results of the Sen's slope estimator showed that during 33 years, the highest rate of change of temperature variables, related to the absolute minimum, was 3.59 ° C in Kish Island. The maximum rainfall in Bushehr-coastal increased by 16.5 mm and the frequency of days with rainfall decreased by 11 days.

Decadal Climate Prediction Project (DCPP), is one of the ambitious programs to bridge the gap between climate prediction and climate. In order to provide climate services to stakeholders, the IRIMO provides daily and seasonal forecasts and climate projections. In the meantime, providing annual prediction has been one of the main requests of users from IRIMO, the gap of annual prediction was evident in previous years. In this study, Iran’s precipiation prediction for the period 2019-2023 were predictedusing the post-processing of the Decadal Climate Prediction Project (DCPP). For this purpose, two types of data have been used, including: output of DCPP project models in historical (1989-2018) and prediction (2019-2023) periods and observed precipitation data from GPCC, a grided databases as an alternative to observational (quasi-observational) data. The results showed that, Iran’s average precipitation in the period 2019-2023 will be normal to less than normal based on 4 methods of bias correction, multi-model weighting, probability prediction and climatic teleconections. As an average, findings of this project showed that the mean precipitation of Iran in the period 2019-2023 will be in the range of normal to less than normal, based on the DCPP model outputs and two decadal scale teleconnections of AMO and PDO. Based on bias correction and weighting system, precipitation in the western and southern half of the Iran will be more than normal and in the east it is normal to less than normal, in the probabilistic method precipitation in 2019 and 2020 preicted to be more than normal and in 2021- 2023, it will be less than normal to normal. Also, the average precipitation in the period of 2019-2023 will be in the range of less than normal, based on the teleconnection method.

Monitoring of flood expansion maps using radar images (SAR) (Case study: Flood Flood in March 2019, Aq Qala city)AbstractFloods are one of the most important hazards that depending on the intensity of rainfall and other effective factors cause great damage to urban and rural areas.The use of radar data is one of the newest and most effective methods in flood study.The exact details of the floods can be studied and the extent of their spread can be determined so that it can be used in future planning.In this study, the flooded areas of Aqqala city and the surrounding villages have been identified by Sentinel 1 data from March 23 to April 4.The aim of this study is to produce maps that extract flood spread from radar (sar) images and show the extent of flood spread in April 2017.Snap, arcgis and envi software have been used as research tools.The results of the research showed that on March 23, 115 square kilometers in the study area and on March 29, 107 square kilometers were submerged due to floods in April 1998. Also, the results of radar images and field visits showed that the main reasons for flooding in the area, heavy rainfall in a few days, flooding of the dam, low slope, lack of river dredging, high bridges on the river and low height of bridges and high percentage of clay in The soil of the area has been.Keywords: Flood, Radar Images, Sentinel 1, Gorganrood River, Aq Qala