Abstract
The drought phenomenon is not specific to the 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 in the period of 29 years (1990-2018). For modeling of TIBI fuzzy index, first, four indicators (SET, SPI, SEB, MCZI) were fuzzy in Matlab software. Then the indices were compared and Topsis model were 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 four of the above indicators with high accuracy. Among these five climatic parameters used in this study, the temperature and precipitation parameters had the most influential effect on the fluctuation of drought severity. The severity of drought was more based on a 6-month scale modeling than 12 months. The highest percentage of drought occurrence was at Bandar Abbas station with a value of 24.3 on a 12-month scale and the lowest was in Shahrekord station with a percentage of 0.36% on a six-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 severely affected by drought, which requires suitable water management in these areas.
Highlights
Drought is one of the natural hazards that is dominated by climate change
The investigation conducted by Alizadeh et al [7], in a research named at the modeling of dispersion of drought caused by climate change in Iran using dynamic system conclude that at all stations, the values of evapotranspiration of the reference plant) increased from January to July, fell to December, and all stations reached their maximum levels in July
In order to study the regional climatic models (RCMS), Um et al [16] examined the observed drought characteristics based on the SPEL in Central Asia, and the results showed that RCMs are correct in humid areas, but in arid areas is incorrect and this model cannot achieve drought events for large spatial scales
Summary
Drought is one of the natural hazards that is dominated by climate change. Drought is one of the most important natural disasters affecting agriculture and water resources [1]. Komasi et al [10] conducted a drought prediction with SPI and EDI indexes using ANFIS modeling method in Kohgiluyeh and Boyerahmad province. Kasi et al [18], in their research, analyzed the dry and wet conditions using RCM and concluded that uncertainty exists in weather forecasts According to their results, probably dryer summers will occur in the southern regions and more severe precipitation will occur in the winter and autumn in the northern regions of the study area in the future. The researchers conducted this research to model, monitor and predict the drought with the new method in Iran In these relationships, xij represents the standardized value, xj the desired index value, xjmax the maximum value in the number series, and xjmin represents the lowest value in the numeric series [19]. In drought monitoring based on TIBI, trend, the severity of persistence and frequency of drought occurrence were
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