Improved Hidden Markov Model Incorporated with Copula for Probabilistic Seasonal Drought Forecasting
Publication: Journal of Hydrologic Engineering
Volume 25, Issue 6
Abstract
Drought is a natural hazard driven by extreme macroclimatic variability, and generally resulting in serious damage to the environment over a sizable area. Accurate, reliable, and timely forecasting of drought behavior plays a key role in early warning of drought management. In this study, a hybrid hidden Markov model coupled with multivariate copula (HMC) is proposed for probabilistic drought forecast. It is an extension of the regular hidden Markov model (HMM) in which the mixture distribution for each forecast is a weighted combination of posterior copula conditional distributions, which are allowed to vary with different predictors. Bayesian inference is used to optimize model structure and parameters. The cascaded sampling procedure is used to obtain conditional probability of a pair copula. The HMC model is performed for multistep meteorological drought forecast at the stations of Hanchuan and Tianmen, China, with the widely used Standardized Precipitation Index (SPI) time series. HMM, artificial neural network (ANN), and autoregressive moving average (ARMA) drought forecasting are also implemented for comparison. Results demonstrate that HMC drought forecast is much more accurate than HMM, ARMA, and ANN for point forecasts as well as interval forecasts. This study is of great significance for understanding drought uncertainty and extending drought early warning.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (51809242), the Fundamental Research Funds for the Central Universities (G1323541875), and the Project of Hubei Key Laboratory of Regional Development and Environmental Response (Hubei University) (2018A003). Special thanks are given to the anonymous reviewers and editors for their constructive comments.
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Published In
Journal of Hydrologic Engineering
Volume 25 • Issue 6 • June 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Feb 14, 2019
Accepted: Oct 23, 2019
Published online: Mar 18, 2020
Published in print: Jun 1, 2020
Discussion open until: Aug 18, 2020
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