Probabilistic Assessment of Drought Characteristics Using Hidden Markov Model
Publication: Journal of Hydrologic Engineering
Volume 18, Issue 7
Abstract
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.
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Acknowledgments
Studies of the authors were supported in part by the National Science Foundation under Grants DBI 0619086, OCI 0753116, and AGS 1025430. This support is gratefully acknowledged. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Information & Authors
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Published In
Journal of Hydrologic Engineering
Volume 18 • Issue 7 • July 2013
Pages: 834 - 845
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Nov 29, 2011
Accepted: Aug 7, 2012
Published online: Aug 18, 2012
Published in print: Jul 1, 2013
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