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KSCE Journal of Civil Engineering

, Volume 22, Issue 1, pp 365–372 | Cite as

Probabilistic assessment of meteorological drought over South Korea under RCP scenarios using a hidden Markov model

  • Jisoo Yu
  • Yei Jun Park
  • Hyun-Han Kwon
  • Tae-Woong KimEmail author
Water Resources and Hydrologic Engineering
  • 137 Downloads

Abstract

Most drought indices are evaluated based on pre-defined thresholds, which are inadequate for demonstrating the inherent uncertainty of drought. This study employed a hidden Markov model-based drought index (HMM-DI) for probabilistic assessment of meteorological drought in South Korea. The HMM-DI was developed to take into account the inherent uncertainty embedded in daily precipitation and to assess drought severity without using pre-defined thresholds. Daily rainfall data recorded during 1973–2015 at 56 stations over South Korea were aggregated with 6- and 12-month windows to develop HMM-DIs for various time scales. The HMM-DIs were extended to assess future droughts in South Korea using synthesized monthly rainfall data (2016–2100) under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The overall results indicated that the HMM-DI can classify drought conditions considering inherent uncertainty embedded in observations and can also demonstrate the probabilistic drought occurrence in the future.

Keywords

climate change drought hidden Markov model rainfall 

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References

  1. Deng, L., Wu, J., Droppo, J., and Acero, A. (2005). “Dynamic compensation of HMM variances using the feature enhancement uncertainty computed from a parametric model of speech distortion.” IEEE Transactions on Speech and Audio Processing, Vol 13, No. 3, pp. 412–421, DOI: 10.1109/tsa.2005.845814.CrossRefGoogle Scholar
  2. Hobbs, B. F. (1997). “Bayesian methods for analyzing climate change and water resource uncertainties.” Journal of Environmental Management, Vol. 49, No. 1, pp. 53–72, DOI: 10.1006/jema.1996.0116.CrossRefGoogle Scholar
  3. Kehagias, A. (2004). “A hidden Markov model segmentation procedure for hydrological and environmental time series.” Stochastic Environmental Research and Risk Assessment, Vol. 18, No. 2, pp. 117–130, DOI: 10.1007/s00477-003-0145-5.CrossRefzbMATHGoogle Scholar
  4. Mallya, G., Tripathi, S., Kirshner, S., and Govindaraju, R. (2013). “Probabilistic assessment of drought characteristics using hidden Markov model.” Journal of Hydrologic Engineering, Vol. 18, No. 7, pp. 834–845, DOI: 10.1061/(asce)he.1943-5584.0000699.CrossRefGoogle Scholar
  5. Miller, D. R. H., Leek, T., and Schwartz, R. M. (1999). “A hidden Markov model information retrieval system.” Proc., 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, pp. 214–221.Google Scholar
  6. Mishra, A. K. and Singh, V. P. (2010). “A review of drought concepts.” Journal of Hydrology, Vol. 391, Nos. 1-2, pp. 202–216, DOI: 10.1016/j.jhydrol.2010.07.012.CrossRefGoogle Scholar
  7. Moradkhani, H., Hsu, K.L., Gupta, H., and Sorooshian, S. (2005). “Uncertainty assessment of hydrology model states and parameters: Sequential data assimilation using the particle filter.” Water Resources Research, Vol. 41, No. 5, W05012, DOI: 10.1029/2004WR003604.CrossRefGoogle Scholar
  8. National Institute of Meteorological Research (2011). Strategies for Climate Change by the IPCC Fifth Assessment Report 2011, Climate Research Laboratory, Seoul.Google Scholar
  9. O’Connell, D. R. H. (2005). “Nonparametric Bayesian flood frequency estimation.” Journal of Hydrology, Vol. 313, No. 1-2, pp. 79–96, DOI: 10.1016/j.jhydrol.2005.02.005.CrossRefGoogle Scholar
  10. Rabiner, L. R. (1989). “A tutorial on hidden Markov models and selected applications in speech recognition.” Proc., IEEE, Vol. 77, No. 2, pp. 257–286.CrossRefGoogle Scholar
  11. Robertson, A. W., Kirshner, S., and Smyth, P. (2004). “Downscaling of daily rainfall occurrence over northeast Brazil using a hidden Markov model.” Journal of Climate, Vol. 17, No. 22, pp. 4407–4424, DOI: 10.1175/jcli-3216.1.CrossRefGoogle Scholar
  12. Seymore, K., McCallum, A., and Rosenfeld, R. (1999). “Learning hidden Markov model structure for information extraction.” In AAAI 99 Workshop on Machine Learning for Information Extraction, pp. 37–42.Google Scholar
  13. Thyer, M. and Kuczera, G. (2000). “Modelling long-term persistence in hydro-climatic time series using a hidden state Markov model.” Water Resources Research, Vol. 36, No. 11, pp. 3301–3310, DOI: 10.1029/2000wr900157.CrossRefGoogle Scholar
  14. Thyer, M. and Kuczera, G. (2003). “A hidden Markov model for modelling long-term persistence in multi-site rainfall time series 1. Model calibration using a Bayesian approach.” Journal of Hydrology, Vol. 275, Nos. 1-2, pp. 12–26, DOI: 10.1016/s0022-1694(02)00412-2.CrossRefGoogle Scholar
  15. World Meteorological Organization (2006). Drought Monitoring and Early Warning: Concepts, Progress and Future Challenges. WMO No. 1006.Google Scholar
  16. Zargar, A., Sadiq, R., and Naser, B. (2011). “A review of drought indices.” Environmental Reviews, Vol. 19, pp. 333–349, DOI: 10.1139/a11-013.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Jisoo Yu
    • 1
  • Yei Jun Park
    • 1
  • Hyun-Han Kwon
    • 2
  • Tae-Woong Kim
    • 3
    Email author
  1. 1.Dept. of Civil and Environmental EngineeringHanyang UniversitySeoulKorea
  2. 2.Dept. of Civil EngineeringChonbuk National UniversityJeonjuKorea
  3. 3.Dept. of Civil and Environmental EngineeringHanyang UniversityAnsanKorea

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