한국농공학회논문집 (Journal of The Korean Society of Agricultural Engineers)

  • 제58권5호
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  • Pages.1-10
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  • 2016
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  • 1738-3692(pISSN)
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  • 2093-7709(eISSN)
한국농공학회 (The Korean Society of Agricultural Engineers)
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비정상성 강우모의기법을 이용한 가뭄 예측기법 개발

Development of Drought Forecasting Techniques Using Nonstationary Rainfall Simulation Method

  • Kim, Tae-Jeong (Chonbuk National University, Department of Civil Engineering) ;
  • Park, Jong-Hyeon (Chonbuk National University, Department of Civil Engineering) ;
  • Jang, Seok-Hwan (Daejin University, Department of Civil Engineering) ;
  • Kwon, Hyun-Han (Chonbuk National University, Department of Civil Engineering)
  • 투고 : 2016.08.17
  • 심사 : 2016.08.27
  • 발행 : 2016.09.30
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초록

Drought is a slow-varying natural hazard that is characterized by various factors such that reliable drought forecasting along with uncertainties estimation has been a major issue. In this study, we proposed a stochastic simulation technique based scheme for providing a set of drought scenarios. More specifically, this study utilized a nonstationary Hidden markov model that allows us to include predictors such as climate state variables and global climate model's outputs. The simulated rainfall scenarios were then used to generate the well-known meteorological drought indices such as SPI, PDSI and PN for the three dam watersheds in South Korea. It was found that the proposed modeling scheme showed a capability of effectively reproducing key statistics of the observed rainfall. In addition, the simulated drought indices were generally well correlated with that of the observed.

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참고문헌

  1. Bengio, Y., and P. Frasconi, 1995. An input output HMM architecture. Advances in neural information processing systems: 427-434.
  2. Bracken, C., B. Rajagopalan, and E. Zagona, 2014. A hidden markov model combined with climate indices for multidecadal streamflow simulation. Water Resources Research 50(10): 7836-7846. https://doi.org/10.1002/2014WR015567
  3. Edward, D., and T. McKee, 1997. Characteristics of 20th century drought in united state at multiple time scales. Climatology Report, Colorado State University, Fort Collins.
  4. Hewitt, C. D., and D. J. Griggs, 2004. Ensembles-based predictions of climate changes and their impacts (ENSEMBLES). Eos 85(52): 566. https://doi.org/10.1029/2004EO520005
  5. Hughes, J. P., and P. Guttorp, 1994. Incorporating spatial dependence and atmospheric data in a model of precipitation. Journal of applied meteorology 33(12): 1503-1515. https://doi.org/10.1175/1520-0450(1994)033<1503:ISDAAD>2.0.CO;2
  6. Pai, D. S., L. Sridhar, P. Guhathakurta, and H. R. Hatwar, 2011. District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI). Natural hazards 59(3): 1797-1813. https://doi.org/10.1007/s11069-011-9867-8
  7. Kim, G. S., and H. G. Park, 2010. Estimation of drought index using CART algorithm and satellite data. Journal of the Korean Association of Geographic Information Studies, 13(1): 128-141 (in Korean).
  8. Kim, T. J., K. Y. Kim, and H. H. Kwon, 2015. Development of multisite spatio-temporal downscaling model for rainfall using GCM mulit model ensemble. Journal of the Korean Society of Civil Engineers, 35(2): 327-340 (in Korean). https://doi.org/10.12652/Ksce.2015.35.2.0327
  9. Kim, T. J., H. H. Kwon, D. R. Lee, and S. K. Yoon, 2014. Development of stochastic downscaling method for rainfall data using GCM. Journal of Korea Water Resources Association, 47(9): 825-838 (in Korean). https://doi.org/10.3741/JKWRA.2014.47.9.825
  10. Kwon, H. H., T. J. Kim, S. H. Hwang, and T. W. Kim, 2013a. Development of daily rainfall simulation model based on homogeneous hidden markov chain. Journal of the Korean Society of Civil Engineers, 33(5): 1861-1870 (in Korean). https://doi.org/10.12652/Ksce.2013.33.5.1861
  11. Kwon, H. H., T. J. Kim, O. K. Kim, and D. R. Lee, 2013b. Development of multi-site rainfall simulation based on homegeneous hidden markov chain model coupled with chow-liu tree Structures. Journal of Korea Water Resources Association, 46(10): 1029-1040 (in Korean). https://doi.org/10.3741/JKWRA.2013.46.10.1029
  12. Lardet, P., and C. Obled, 1994. Real-time flood forecasting using a stochastic rainfall generator. Journal of Hydrology 162(3): 391-408. https://doi.org/10.1016/0022-1694(94)90238-0
  13. Mallya, G., S. Tripathi, S. Kirshner, and R. S. Govindaraju, 2012. Probabilistic assessment of drought characteristics using hidden Markov model. Journal of Hydrologic Engineering 18(7): 834-845. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000699
  14. McKee, T. B., N. J. Doesken, and J. Kleist, 1993. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology 17(22): 179-183.
  15. McKee, T. B., N. J. Doesken, and J. Kleist, 1995. Drought monitoring with multiple time scales. In Proceedings of the 9th Conference on Applied Climatology: 233-236.
  16. Meila, M. and M. I. Jordan, 1996. Markov mixtures of experts. Multiple Model Approaches to Modelling and Control: 145-166.
  17. Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996. The ECMWF ensemble prediction system: methodology and validation. Quarterly journal of the royal meteorological society 122(529): 73-119. https://doi.org/10.1002/qj.49712252905
  18. Palmer, W. C. 1965. Meteorological drought. Washington, DC, USA: US Department of Commerce, Weather Bureau.
  19. Posada, D. and T. R. Buckley, 2004. Model selection and model averaging in phylogenetics : advantages of akaike information criterion and bayesian approaches over likelihood ratio tests. Systematic biology 53(5): 793-808. https://doi.org/10.1080/10635150490522304
  20. Silburn, D. M., and R. D. Connolly, 1995. Distributed parameter hydrology model (ANSWERS) applied to a range of catchment scales using rainfall simulator data I : Infiltration modelling and parameter measurement. Journal of Hydrology 172(1): 87-104. https://doi.org/10.1016/0022-1694(95)02740-G
  21. Thornthwaite, C. W., and J. R. Mather, 1955. The water budget and its use in irrigation. In Water, The Yearbook of Agriculture. US Department of Agriculture: 346-358.
  22. Wilhite, D. A., and M. H., Glantzb, 1985. Understanding : the Drought Phenomenon: The Role of Definitions. Water International 10(3): 111-120. https://doi.org/10.1080/02508068508686328
  23. Willeke, G., J. R. M. Hosking, J. R. Wallis, and N. B. Guttman, 1994. The national drought Atlas. Institute for water resources rep. U.S. Army Corps of Engineers.
  24. Yoo, J. Y., J. Y. Kim, H. H. Kwon, and T. W. Kim, 2014b. Sensitivity assessment of meteorological drought index using bayesian network. Journal of the Korean Society of Civil Engineers 34(6): 1787-1796 (in Korean). https://doi.org/10.12652/Ksce.2014.34.6.1787
  25. Yoo, J. Y., H. H. Kwon, T. W. Kim, and S. O. Lee, 2014a. Probabilistic assessment of drought characteristics based on homogeneous hidden markov model. Journal of the Korean Society of Civil Engineers 34(1): 145-153 (in Korean). https://doi.org/10.12652/Ksce.2014.34.1.0145