Dispersion modeling drought caused by climate change in Iran using system dynamics

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.