Drought Monitoring and Prediction in Kashmar County Using SPI Index and Markov Chain
Drought is a natural and mysterious creeping phenomenon, and many believe that it has a complex mechanism and is less known than other natural disasters. Studying drought events is very important for natural and water resource management planning. One strategy to manage drought is to predict drought conditions using a probabilistic tool. The study aimed to predict the probability and severity of meteorological drought in Kashmar. To this aim, the monthly rainfall data of the Kashmar Synoptic Station was used to analyze a 30-year period (2017-1987). The drought status of Kashmar County was considered by drought duration of The Standardized Precipitation Index (SPI) at 1, 3, 6, 9, 12, 18, 24, and 48-month timescales. Next, using the Markov Chain, the transition probability matrix for the study area was carried out, and the probability of meteorological droughts was predicted for severity. The results of this study showed that the severest drought in Kashmar occurred in 2000 and 2009, with an SPI coefficient of greater than -3, while the highest precipitation occurred in 1993, with an SPI coefficient of 2.8. Then, the Markov chain model was used to calculate the balance probabilities for dry, wet, and normal periods at different time scales. The results showed that, on average, the stationary probability of dry, normal, and wet periods is 29, 30, and 41 percent, respectively. Consequently, it means that the region's climate conditions are often normal. As a result, given the critical situation of Kashmar County, the opportunity to reduce water stress and aquifer discharge can be exploited.