Stochastic Simulation of Drought Severity Based on Palmer Index



Drought is a gradual phenomenon and cause important changes in water resources, agriculture and so on. Due to the stochastic behavior of factors that influence the occurrence and intense of drought, one can consider this phenomenon as a stochastic process. With respect to the importance of predicted drought severity and its vital influence in designing and operating water resources systems, this article includes agricultural drought monitoring in 34 years (1971-2004) and use stochastic modeling to predict agricultural drought based on Palmer Drought Severity Index (PDSI) in the synoptic station of Mashhad. Results of drought monitoring in 34 years show that more than 64% of months were drought and also in the recent years, duration and severity of drought are increased that in the 50% of preliminary of data (1971-1988), sum of palmer index was 129.81 and in the residual years it was
-241.07, it means that more than 285 percent change has been observed. Using Time series modeling based on Box and Jenkins approach, with three stages; model identification, parameter estimation and diagnostic checking, finally the SARIMA(1,0,0)(0,1,1)12 was developed. Also, the stochastic model developed to predict drought was found to give reasonably good results up to 2-month lead time.