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Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection

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Abstract

The creeping characteristics of drought make it possible to mitigate drought’s effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, we proposed a new probabilistic scheme to forecast droughts that used a discrete-time finite state-space hidden Markov model (HMM) aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The standardized precipitation index (SPI) with a 3-month time scale was employed to represent the drought status over the selected stations in South Korea. The new scheme used a reversible jump Markov chain Monte Carlo algorithm for inference on the model parameters and performed an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to perform a probabilistic forecast of SPI at the 3-month time scale that considered uncertainties. The point forecasts which were derived as the HMM-RCP forecast mean values, as measured by forecasting skill scores, were much more accurate than those from conventional models and a climatology reference model at various lead times. We also used probabilistic forecast verification and found that the HMM-RCP provided a probabilistic forecast with satisfactory evaluation for different drought categories, even at long lead times. In a drought event analysis, the HMM-RCP accurately predicted about 71.19 % of drought events during the validation period and forecasted the mean duration with an error of less than 1.8 months and a mean severity error of <0.57. The results showed that the HMM-RCP had good potential in probabilistic drought forecasting.

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Acknowledgments

This study was supported by a Grant (14AWMP-B082564-01) from the Water Management Research Program funded by Ministry of Land, Infrastructure and Transport, Republic of Korea.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Hanyang University, Seoul, 04763, Republic of Korea

    Si Chen & Ji Yae Shin

  2. Department of Civil and Environmental Engineering, Hanyang University, Ansan, 15588, Republic of Korea

    Tae-Woong Kim

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  1. Si Chen
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  2. Ji Yae Shin
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Correspondence to Tae-Woong Kim.

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Chen, S., Shin, J.Y. & Kim, TW. Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection. Stoch Environ Res Risk Assess 31, 1061–1076 (2017). https://doi.org/10.1007/s00477-016-1279-6

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