Droughts are evaluated using drought indices that measure the departure of meteorological and hydrological variables such as precipitation and stream flow from their long-term averages. While there are many drought indices proposed in the literature, most of them use pre-defined thresholds for identifying drought classes ignoring the inherent uncertainties in characterizing droughts. In this study, a hidden Markov model (HMM)  is developed for probabilistic classification of drought states. The HMM captures space and time dependence in the data. The proposed model is applied to assess drought characteristics in Indiana using monthly precipitation and stream flow data. The comparison of HMM based drought index with standard precipitation index (SPI)  suggests that the HMM index provides more intuitive results.
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