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Probabilistic Assessment of Drought Characteristics Using Hidden Markov Model
Droughts are characterized by drought indexes that measure the departures of meteorological and hydrological variables, such as precipitation and streamflow, from their long-term averages. Although many drought indexes have been proposed in the literature, most use predefined thresholds for identifying drought classes, ignoring the inherent uncertainties in characterizing droughts. This study employs a hidden Markov model (HMM) for the probabilistic classification of drought states. Apart from explicitly accounting for the time dependence in the drought states, the HMM-based drought index (HMM-DI) provides model uncertainty in drought classification. The proposed HMM-DI is used to assess drought characteristics in Indiana by using monthly precipitation and streamflow data. The HMM-DI results were compared to those from standard indexes and the differences in classification results from the two models were examined. In addition to providing the probabilistic classification of drought states, the HMM is suited for analyzing the spatio-temporal characterization of droughts of different severities.
Probabilistic Assessment of Drought Characteristics Using Hidden Markov Model
Droughts are characterized by drought indexes that measure the departures of meteorological and hydrological variables, such as precipitation and streamflow, from their long-term averages. Although many drought indexes have been proposed in the literature, most use predefined thresholds for identifying drought classes, ignoring the inherent uncertainties in characterizing droughts. This study employs a hidden Markov model (HMM) for the probabilistic classification of drought states. Apart from explicitly accounting for the time dependence in the drought states, the HMM-based drought index (HMM-DI) provides model uncertainty in drought classification. The proposed HMM-DI is used to assess drought characteristics in Indiana by using monthly precipitation and streamflow data. The HMM-DI results were compared to those from standard indexes and the differences in classification results from the two models were examined. In addition to providing the probabilistic classification of drought states, the HMM is suited for analyzing the spatio-temporal characterization of droughts of different severities.
Probabilistic Assessment of Drought Characteristics Using Hidden Markov Model
Mallya, Ganeshchandra (author) / Tripathi, Shivam (author) / Kirshner, Sergey (author) / Govindaraju, Rao S. (author)
Journal of Hydrologic Engineering ; 18 ; 834-845
2012-08-18
122013-01-01 pages
Article (Journal)
Electronic Resource
English
Probabilistic Assessment of Drought Characteristics Using Hidden Markov Model
| British Library Online Contents | 2013
Probabilistic Assessment of Drought Characteristics Using Hidden Markov Model
| Online Contents | 2013