Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection
- Authors
- Chen, Si; Shin, Ji Yae; Kim, Tae-Woong
- Issue Date
- Jul-2017
- Publisher
- Springer Verlag
- Keywords
- Drought forecasting; Hidden Markov model; RCP climate scenario; SPI
- Citation
- Stochastic Environmental Research and Risk Assessment, v.31, no.5, pp.1061 - 1076
- Indexed
- SCIE
SCOPUS
- Journal Title
- Stochastic Environmental Research and Risk Assessment
- Volume
- 31
- Number
- 5
- Start Page
- 1061
- End Page
- 1076
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/9481
- DOI
- 10.1007/s00477-016-1279-6
- ISSN
- 1436-3240
- 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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