The drought phenomenon is not specific to a region and it affects different parts of the world. One of these areas is Iran in southwest Asia, which suffered from this phenomenon in recent years. The purpose of this study is to model, analyze and predict the drought in Iran. To do this, climatic parameters (precipitation, temperature, sunshine, minimum relative humidity and wind speed) were used at 30 stations for a period of 29 years (1990–2018). For modelling of the Combined Indicateurs based on four indices, StandardizedEvapotranspiration Torrent White Index (SET), Standardized Precipitation Index (SPI), Standardized Evapotranspiration Blanney Creedal FAO Index (SEB) and Modified CZI Index (MCZI) were fuzzy in Matlab software. Then the indices were compared and the Topsis model was used for prioritizing areas involved with drought. Finally, Anfis adaptive artificial neural network model was used to predict. Results showed that the new fuzzy index TIBI for classifying drought reflected above four indicators with high accuracy. Of these five climatic parameters: (precipitation, temperature, sunshine, minimum relative humidity and wind speed) used in this study, the temperature and precipitation parameters had the mosteffect on the fluctuation of drought severity. The severity of the drought was more based on 6-month scale modelling than 12 months. The highest percentage of drought occurrence was at Bandar Abbas station with a value of 24.30 on a 12-month scale and the lowest was in Shahrekord station with a percentage of 0.36% on a 6-month scale. Based on Anfis model and TIBI fuzzy index, Bandar Abbas, Bushehr and Zahedan stations were more exposed to drought due to the TIBI index of 0.62, 0.96 and 0.97, respectively. According to the results in both 6 and 12 months scale, the southern regions of Iran were more severelyaffected by drought, which requires suitable water management in these areas.
Volume 129, 2020
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