Forecasting of meteorological drought using Hidden Markov Model (case study: The upper Blue Nile river basin, Ethiopia)

An improved drought management must rely on an accurate monitoring and forecasting of the phenomenon in order to activate appropriate mitigation measures. In this study, several homogenous Hidden Markov Models (HMMs) were developed to forecast droughts using the Standardized Precipitation Index, SPI, at short-medium term. Validation of the developed models was carried out with reference to precipitation series observed in 22 stations located in the upper Blue Nile river basin. The performance of the HMM was measured using various forecast skill criteria. Results indicate that Hidden Markov Model provides a fairly good agreement between observed and forecasted values in terms of the SPI time series on various lead time. Results seem to confirm the reliability of the proposed models to discriminate between events and non-events relatively well, thus suggesting the suitability of the proposed procedure as a tool for drought management and drought early warning.