干旱区生态环境知识资源中心

Investigation of drought event has a great importance in the natural resources management and planning water resources management. One strategy to manage drought is to predict drought conditions by probabilistic tools. In this study climate data of 11 synoptic stations in south of Iran during 1980-2014 were used to estimate of seasonal drought based on RDI index. To prediction of drought (from 2015 to 2020) and analysis of changes trend of it, time series model, first-order Markov Chain model and parametric and non- parametric statistical methods were used. Results showed that MA (5), MA (10), AR (12) and AR (15) were the best time series models that fitted in data of all stations. According to results of prediction of drought classes, classes with normal and moderate dry condition had allocated the most frequency of seasonal drought classes from 2015 to 2020 based on time series model and Markov Chain method. Analysis of changes trend of drought classes showed that based on observed data (1980-2014) and predicted data (1980-2020) changes trend of drought classes in all stations had increasing trend based on parametric and non- parametric statistical methods but increasing trend in about 27% of stations include: Bandar Abbas, Bandar Lengeh, Jask and Shiraz had significant level of 5%. Finally result showed that the study area in 2020 compared to 2014 will be drier.