KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
권호 표지
DOI QR코드

DOI QR Code

Probabilistic Assessment of Drought Characteristics based on Homogeneous Hidden Markov Model

동질성 은닉 마코프 모형을 적용한 가뭄특성의 확률론적 평가

  • 유지영 (전북대학교 토목공학과) ;
  • 권현한 (전북대학교 토목공학과) ;
  • 김태웅 (한양대학교 건설환경플랜트공학과) ;
  • 이승오 (홍익대학교 건설도시공학부)
  • Received : 2013.09.05
  • Accepted : 2013.11.13
  • Published : 2014.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

Several studies regarding drought indices and criteria have been widely studied in the literature. If one defines the onset, severity, and end of droughts, in general, a certain threshold needs to be set to assess the drought events. However, the uncertainty associated with the threshold is a critical problem in drought analysis. To take full advantage of the inherent features in the rainfall series, a Hidden Markov Model (HMM) based probabilistic drought analysis was proposed rather than using the existing threshold based analysis. As a result, the proposed HMM based probabilistic drought analysis scheme shows better performance in terms of defining drought state and understanding underlying characteristics of the drought. In addition, the HMM based approach is capable of quantifying the uncertainties associated with the classifying meteorological drought condition in a systematic way.

현재 국내외적으로 다양한 가뭄지수들이 개발과 더불어 가뭄평가를 위한 다양한 기준이 개발되고 있다. 가뭄지수를 이용하여 가뭄의 시작, 강도 및 종료 시점을 정의할 경우, 일반적으로 특정 기준값(threshold)에 근거한 해석이 이루어지고 있으나, 이는 실제 가뭄을 평가하는데 불확실성을 가중시키는 원인으로 작용하고 있다. 따라서 본 연구에서는 기존의 가뭄지수 기반의 가뭄판단 시 적용되어져 왔던 특정한 기준값에 근거한 해석이 아닌, 기상학적 가뭄발생의 주된 원인 중 하나인 월강수량 자료에 내재되어 있는 특징을 최대한 활용하고자 은닉 마코프 모형(Hidden Markov Model, HMM) 기반의 확률론적 가뭄해석기법을 제안하였다. 그 결과, 본 연구에서 제시한 HMM 기반의 확률론적 가뭄해석기법은 기존의 다양한 가뭄지수를 적용한 가뭄해석과 비교하여 기상학적 측면에서의 가뭄판단의 명확한 기준 제시 및 가뭄발생의 원인을 규명하는 데 있어 체계적으로 불확실성을 감안한 해석이 가능하였다.

Keywords

References

  1. Dracup, J. A., Lee, K. S. and Paulson, E. G. Jr. (1980). "On the definition of droughts." Water Resources Research, Vol. 16, No. 2, pp. 297-302. https://doi.org/10.1029/WR016i002p00297
  2. Hughes, J. P. and Guttorp. P. (1994). "A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena." Water Resour. Res., Vol. 30, No. 5, pp. 1535-1546. https://doi.org/10.1029/93WR02983
  3. Kwon, H. H., Kim, T. J., Hwang, S. H. and Kim, T. W. (2013a). "Development of daily rainfall simulation model based on homogeneous hidden markov chain." Journal of the Korean Society of Civil Engineers, Vol. 33, No. 5, pp. 1861-1870 (in Korean). https://doi.org/10.12652/Ksce.2013.33.5.1861
  4. Kwon, J., Ahn, J. H. and Kim, T. W. (2013b). "Development on classification standard of drought severity." Journal of Korean Water Resources Association, Vol. 46, No. 2, pp. 195-204 (in Korean). https://doi.org/10.3741/JKWRA.2013.46.2.195
  5. Loaiciga, H. and Leipnik, R. (1996). "Stochastic renewal model of low-flow streamflow sequences." Stochastic Hydrology and Hydraulics, Vol. 10, No. 1, pp. 65-85. https://doi.org/10.1007/BF01581794
  6. Mallya, G., Shivam Tripathi, A. M., Kirshner, S. and Rao S. Govindaraju, M. (2013). "Probabilistic assessment of drought characteristics using hidden markov model." Journal of Hydrologic Engineering, Vol. 18, No. 7, DOI: 10.1061/(ASCE)HE.1943-5584.0000699.
  7. Matsuyama, Y. (2011). "Hidden markov model estimation based on alpha-EM algorithm: Discrete and continuous alpha-HMMs." International Joint Conference on Neural Network, San Jose, Vol. 7, No. 5, pp. 808-816.
  8. Mishra, A. K. and Desai, V. R. (2005). "Drought forecasting using stochastic models." Stochastic Environmental Research and Risk Assessment, Vol. 19, No. 5, pp. 326-339. https://doi.org/10.1007/s00477-005-0238-4
  9. Mishra, A. K. and Singh, V. P. (2010). "A review of drought concepts." Journal of Hydrology, Vol. 391, No. 1-2, pp. 202-216. https://doi.org/10.1016/j.jhydrol.2010.07.012
  10. Sen, Z. (1980). "Statistical analysis of hydrologic critical droughts." Journal of the Hydraulics Division, Vol. 106, No. 1, pp. 99-115.
  11. Steinemann, A. (2003). "Drought triggers: A stochastic approach to evaluation." Journal of the American Water Resources Association, Vol. 39, No. 5, pp. 1217-1234. https://doi.org/10.1111/j.1752-1688.2003.tb03704.x
  12. Yevjevich, V. (1967). "On objective approach to definitions and investigations of continental hydrologic droughts." Hydrology Paper, No. 23, Colorado State University, Fort Collins, pp. 4-18.

Cited by

  1. Projection of Temporal Trends on Drought Characteristics using the Standardized Precipitation Evapotranspiration Index (SPEI) in South Korea vol.57, pp.1, 2015, https://doi.org/10.5389/KSAE.2015.57.1.037
  2. Probabilistic Assessment of Hydrological Drought Using Hidden Markov Model in Han River Basin vol.47, pp.5, 2014, https://doi.org/10.3741/JKWRA.2014.47.5.435
  3. Projection of Future Water Supply Sustainability in Agricultural Reservoirs under RCP Climate Change Scenarios vol.56, pp.4, 2014, https://doi.org/10.5389/KSAE.2014.56.4.059
  4. Drought Frequency Analysis Using Hidden Markov Chain Model and Bivariate Copula Function vol.48, pp.12, 2015, https://doi.org/10.3741/JKWRA.2015.48.12.969