In recent decades, dust storms have intensified due to climate changes, including drought and human interventions. In addition to removing soil particles, dust brings storms bring about severe economic, social, and health problems. Due to its strategic conditions – including its status as an industrial and agricultural hub and the province hosting more than 29 million Iranian and international pilgrims, especially during the seasons and months with specific holidays – should be prioritized in the examination of dust storm frequency. The purpose of this study was to examine and track the frequency of dust storms and analyze the effect of climactic changes on them through the investigation of drought trend as well as the trend of changes in the land surface coverage percentage during the statistical analysis period. The results of counting the dust codes showed that dust storms with local origin had the highest frequency. According to the monthly analysis, Sarakhs and Mashhad stations had the most dust phenomena, with the most dusty days in the study area during summer and spring seasons, especially June and July months. The results of regression analysis of drought index and the monthly local dusty days indicated the existence of a significant, positive relationship between dust increase and drought intensity in Mashhad and Sarakhs stations during the statistical period of 27 years at 99 percent confidence interval. Finally, in the light of the results obtained from the examination of the land surface changes, it is recommended that land use plans be used along with short-term methods of soil stabilization in order to reduce the economic damages and preserve people’s health against dust during busy months and seasons so as to achieve the goals of long-term methods.

Drought changes for optimal operation management of water resources well is the sensible. That's why last round of very extensive research on modeling drought in the world and Iran is and using their water projects and has conducted numerous hydraulic. One of the goals dynamic systems modeling potential policies to improve system performance. Modeling SPI index as an indicator of the country's drought situation stations using radial neural network model for each station was done. Independent variables neural network, relative humidity, temperature and lack of objects, which were selected according to their impact on precipitation. SPI index is the dependent variable. In total period of 42 years calculated by SPI, 12-month and 348 standard score by calculating the SPI 24-month, 336 standard score is obtained for each station. At all stations, ETo values from January to July to December increased and then fell in July to its maximum level reached in all stations. The highest average monthly ETo values in Abadan and Ahvaz stations in July and 18/232 and 16/214 mm respectively happened.