Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation and assessment of long-term drought outlook information using the long-term forecasting data

장기예보자료를 활용한 장기 가뭄전망정보 산정 및 평가

  • So, Jae-Min (Department of Civil and Environmental Engineering, Sejong University) ;
  • Oh, Taesuk (Climate Extremes Analysis and Assessment Team, Korea Meteorological Administration) ;
  • Bae, Deg-Hyo (Department of Civil and Environmental Engineering, Sejong University)
  • Received : 2017.07.17
  • Accepted : 2017.09.04
  • Published : 2017.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

The objective of this study is to evaluate the long-term drought outlook information based on long-term forecast data for the 2015 drought event. In order to estimate the Standardized Precipitation Index (SPI) for different durations (3-, 6-, 9-, 12-months), we used the observation precipitation of 59 Automated Synoptic Observing System (ASOS) sites, forecast and hindcast data of GloSea5. The Receiver Operating Characteristic (ROC) analysis and statistical analysis (Correlation Coefficient, CC; Root Mean Square Error, RMSE) were used to evaluate the utilization of drought outlook information for the forecast lead-times (1~6months). As a result of ROC analysis, ROC scores of SPI(3), SPI(6), SPI(9) and SPI(12) were estimated to be over 0.70 until the 2-, 3-, 4- and 5-months. The CC and RMSE values of SPI(3), SPI(6), SPI(9) and SPI(12) for forecast lead-time were estimated as (0.60, 0.87), (0.72, 0.95), (0.75, 0.95) and (0.77, 0.89) until the 2-, 4-, 5- and 6-months respectively.

본 연구에서는 장기예보자료 기반의 장기 가뭄전망정보를 산정하고, 2015년 가뭄사상을 대상으로 활용성을 평가하였다. 이를 위해 ASOS 59개 지점의 관측강수량, GloSea5의 미래예측 및 과거재현 자료를 활용하였으며, 다양한 지속기간(3, 6, 9, 12개월)에 대한 SPI를 산정하였다. 또한 예보선행시간(1~6개월)에 따른 SPI와 관측자료 기반의 SPI 간의 ROC (Receiver Operating Characteristic)및 통계적 분석(상관계수, 평균제곱근 오차)을 수행하여 전망정보의 활용성을 평가하였다. ROC 분석결과, SPI(3)는 2개월, SPI(6)은 3개월, SPI(9)는 4개월, SPI(12)는 5개월까지 ROC score 약 0.70 이상으로 산정되었다. 예보선행시간별 상관계수 및 평균제곱근오차의 경우, 2개월 선행시간 SPI(3)은 0.60, 0.87, 4개월 선행시간 SPI(6)은 0.72, 0.95, 5개월 선행시간 SPI(9)는 0.75, 0.95, 6개월 선행시간 SPI(12)는 0.77, 0.89로 상관계수는 높게, 평균제곱근오차는 낮게 산정되어 활용성이 있는 것으로 판단된다.

Keywords

References

  1. Bae, D. H., Son, K. H., and Kim, H. A. (2013). "Derivation & evaluation of drought threshold level considering hydrometeorological data on South Korea." Atmosphere, Vol. 46, pp. 289-300.
  2. Barnes, S. (1964). "A technique for maximizing details in numerical map analysis." Journal of Applied Meteorology, Vol. 3, No. 4, pp. 395-409.
  3. Bowler, N. E., Arribas, A., Beare, S. E., Mylne, K. R., and Shutts, G. J. (2009). "The local ETKF and SKEB: upgrades to the MOGREPS short-range ensemble prediction system." Quarterly Journal of the Royal Meteorological Society, Vol. 135, pp. 767-776. https://doi.org/10.1002/qj.394
  4. Essery, R. L. H., Best, M. J., Betts, R. A., Cox, P. M., and Taylor, C. M. (2003). "Explicit representation of subgrid heterogeneity in a GCM land surface scheme." Journal of Hydrometeorology, Vol. 4, pp. 530-543. https://doi.org/10.1175/1525-7541(2003)004<0530:EROSHI>2.0.CO;2
  5. Hayes, M., Svoboda, M., Wall, N., and Widhalm, M. (2011). "The Lincoln declaration on drought indices: universal meteorological drought index recommended." Bulletin of the American Meteorological Society, Vol. 92, No. 4, pp. 485-488. https://doi.org/10.1175/2010BAMS3103.1
  6. Hunke, E. C., and Lipscombe, W. H. (2008). CICE: the Los Alamos sea ice model documentation and software user's manual, Version 4.0.
  7. Kim, Y. O., Lee, J. K., and Richard, N. P. (2012). "A drought outlook study in Korea." Hydrological Sciences Journal, Vol. 57, No. 6, pp. 1141-1153. https://doi.org/10.1080/02626667.2012.702212
  8. Madec, G. (2008). "NEMO ocean engine." Institut Piere-Simon Laplace (IPSL), France, No. 27.
  9. Mason, I. B. (1982). "A model for assessment of weather forecasts." Australian Meterological Magazine, Vol. 30, pp. 291-303.
  10. Mckee, T. B., Doesken, N. J., and Kleist, J. (1993). "The relationship of drought frequency and duration to time scales." 8th Conference on Applied Climatology. 17-22 January, Anaheim, California.
  11. Mo, K. C., and Lyon, B. (2015). "Global meteorological drought prediction using the north american multi-model ensembel." Journal of Hydrometeorology, Vol. 16, pp. 1409-1424. https://doi.org/10.1175/JHM-D-14-0192.1
  12. Singh, V., and Fiorentino, M. (1996). "Geographical information systems in hydrology." Kluwer Academic Publishers, pp. 175-194.
  13. Son, K. H., Bae, D. H. and Cheong, H. S. (2015). "Construction & evaluation of GloSea5-based hydrological drought outlook system." Atmosphere. Korean Meteorological Society, Vol. 25, No. 2, pp.271-281.
  14. Stagge, J. H., Kohn, I., Tallaksen, L. M., and Stahl, K. (2015). "Modeling drought impact occurrence based on meteorological drought indices in Europe." Journal of Hydrology, Vol. 530, pp. 37-50. https://doi.org/10.1016/j.jhydrol.2015.09.039
  15. Trenberth, K. E. (1984) "Some effects of finite sample size and persistence on meteorological statistics. Part II: potential predictability." Monthly Weather Review, Vol. 112, pp. 2369-2379. https://doi.org/10.1175/1520-0493(1984)112<2369:SEOFSS>2.0.CO;2
  16. Valcke, S. (2011) OASIS3 user guide (prism 2-5). Tech. Rep. 3 Programme for integrated earth system modelling (PRISM) support initiative.
  17. Vicente-Serrano, S. M., Begueria, S., Lorenzo-Lacruz, J., Camarero, J. J., Lopez-Moreno, J. I., Azorin-Molina, C., Revuelto, J., Moran-Tejeda, E., and Sanchez-Lorenzo, A. (2012). "Performance of drought indices for ecological, agricultural and hydrological applications." Earth Interactions, Vol. 16, pp. 1-27.
  18. Wilhite, D. A., and Glantz, M. H. (1985). "Understanding the drought phenomenon: the role of definition." Water International, Vol. 10, pp. 111-120. https://doi.org/10.1080/02508068508686328
  19. Wilks, D. S. (2006). "Statistical methods in the atmospheric sciences." Academic Press.
  20. Yoon, K. H., Mo, K., and Wood, E. F. (2012). "Dynamicmodelbased seasonal prediction of meteorological drought over the contiguous United States." Journal of Hydrometeorology, Vol. 13, pp. 463-482. https://doi.org/10.1175/JHM-D-11-038.1
  21. Yuan, X., Wood, E. F., Luo, L, and Pan, M. (2011). "A first look at Climate Forecast System version 2 (CFSv2) for hydrological seasonal prediction." Geophysical Research Letters, Vol. 38, L13402, doi: 10.1029/2011GL047792.
  22. Yuan, X., Wood, E. F., Luo, L, Chaney, N. W., Sheffield, J., Kam, J., Liang, M., and Guan. K. (2013). "Probabilistic seasonal forecasting of African drought by dynamical models." Journal of Hydrometeorology, Vol. 14, pp. 1706-1720. https://doi.org/10.1175/JHM-D-13-054.1