Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Bias Correction of RCP-based Future Extreme Precipitation using a Quantile Mapping Method ; for 20-Weather Stations of South Korea

분위사상법을 이용한 RCP 기반 미래 극한강수량 편의보정 ; 우리나라 20개 관측소를 대상으로

  • 박지훈 (서울대학교 농업생명과학대학 생태조경.지역시스템공학부) ;
  • 강문성 (서울대학교 농업생명과학대학 조경.지역시스템공학부, 농업생명과학연구원) ;
  • 송인홍 (서울대학교 농업생명과학연구원)
  • Received : 2012.10.17
  • Accepted : 2012.11.14
  • Published : 2012.11.30
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Abstract

The objective of this study was to correct the bias of the Representative Concentration Pathways (RCP)-based future precipitation data using a quantile mapping method. This method was adopted to correct extreme values because it was designed to adjust simulated data using probability distribution function. The Generalized Extreme Value (GEV) distribution was used to fit distribution for precipitation data obtained from the Korea Meteorological Administration (KMA). The resolutions of precipitation data was 12.5 km in space and 3-hour in time. As the results of bias correction over the past 30 years (1976~2005), the annual precipitation was increased 16.3 % overall. And the results for 90 years (divided into 2011~2040, 2041~2070, 2071~2100) were that the future annual precipitation were increased 8.8 %, 9.6 %, 11.3 % respectively. It also had stronger correction effects on high value than low value. It was concluded that a quantile mapping appeared a good method of correcting extreme value.

Keywords

References

  1. Berg, P., H. Feldmann, H. J. Panitz, 2012. Bias correction of high resolution regional climate model data. Journal of Hydrology 448-449: 80-92. https://doi.org/10.1016/j.jhydrol.2012.04.026
  2. Chen, J., F. P. Brissette, R. Leconte, 2011. Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. Journal of Hydrology 401: 190-202. https://doi.org/10.1016/j.jhydrol.2011.02.020
  3. Gudmundsson, L., J. B. Bremnes, J. E. Haugen, and T. Engen Skaugen, 2012. Technical note: Downscaling RCM precipitation to the station scale using quantile mapping -a comparison of methods. Hydrology and Earth System Sciences Discussions 9: 6185-6201. https://doi.org/10.5194/hessd-9-6185-2012
  4. Hashino, T., A. A. Bradley, and S. S. Schwartz, 2007. Evaluation of bias-correction methods for ensemble streamflow volume forecasts. Hydrology and Earth System Sciences Discussions 11: 939-950. https://doi.org/10.5194/hess-11-939-2007
  5. Hong, E. M., J. Y. Choi, S. H. Lee, S. H. Yoo, and M. S. Kang, 2009. Estimation of paddy rice evapotranspiration considering climate change using LARS-WG. Journal of the Korean Society of Agricultural Engineers 51(3): 25-35 (in Korean). https://doi.org/10.5389/KSAE.2009.51.3.025
  6. IPCC, 2007. Climate Change 2007: Impacts, adaption and vulnerability. Cambridge University Press, Cambridge, UK.
  7. Kang, B. S., and S. J. Moon, 2011. The use of APCC seasonal forecast information for real-time water management and drought prediction. Journal of Korean Society of Hazard Mitigation 11(1): 31-36 (in Korean).
  8. Kim, B. S., B. K. Kim, M. S. Kyung, and H. S. Kim, 2008. Impact assessment of climate change on extreme rainfall and I-D-F analysis. Journal of Korea Water Resources Association 41(4): 379-394 (in Korean). https://doi.org/10.3741/JKWRA.2008.41.4.379
  9. Kim, T. Y., and H. S. Yoon, 2011. Skill assessments for evaluating the performance of the hydrodynamic model. Journal of the Korean Society for Marine Environmental Engineering 14(2): 107-113 (in Korean). https://doi.org/10.7846/JKOSMEE.2011.14.2.107
  10. Lee, J. J., H. H. Kwon, J. Y. Park, and K. T. Yum, 2012. The next generation climate change scenarios for water resource assessment. Magazine of Korea Water Resources Association 45(2): 48-53 (in Korean).
  11. NIMR, 2011. Climate change scenario report for IPCC AR5 (in Korean).
  12. So, B. J., M. J. Kim, and H. H. Kwon, 2012. Forecast and assessment of the next generation climate change scenarios of Korea Meteorological Administration. Magazine of Korea Water Resources Association 45(8): 56-70 (in Korean).
  13. Stedinger, J. R., R. M. Vogel, and E. Foufoula-Georgiou, 1993. Chapter 18: Frequency analysis of extreme events. Handbook of Hydrology, Maidment, D. R. (editor in chief), McGraw-Hill.
  14. Sung, J. H., B. S. Kim, H. S. Kang, and C. H. Cho, 2012. Non-stationary frequency analysis for extreme precipitation based on representative concentration pathways (RCP) climate change scenarios. Journal of Korean Society of Hazard Mitigation 12(2): 231-244 (in Korean). https://doi.org/10.9798/KOSHAM.2012.12.6.231
  15. Sung, J. H., H. S. Kang, S. H. Park, C. H. Cho, D. H. Bae, and Y. O. Kim, 2012. Projection of extreme precipitation at the end of 21st century over South Korea based on representative concentration pathways (RCP). Journal of Atomosphere. Korean Meteorological Society 22(2): 221-231 (in Korean).
  16. Teutschbein, C., and J. Seibert, 2012. Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology 456: 12-29. https://doi.org/10.1016/j.jhydrol.2012.05.052
  17. Yoo, S. H., J. Y. Choi, S. H. Lee, Y. G. Oh, and N. Y. Park, 2012. The impacts of climate change on paddy water demand and unit duty of water using highresolution climate scenarios. Journal of the Korean Society of Agricultural Engineers 54(2): 15-26 (in Korean). https://doi.org/10.5389/KSAE.2012.54.2.015

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