Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Development of Representative GCMs Selection Technique for Uncertainty in Climate Change Scenario

기후변화 시나리오 자료의 불확실성 고려를 위한 대표 GCM 선정기법 개발

  • Jung, Imgook (Climate Services and Research Department, APEC Climate Center) ;
  • Eum, Hyung-Il (Environment Monitoring and Science Division) ;
  • Lee, Eun-Jeong (Climate Services and Research Department, APEC Climate Center) ;
  • Park, Jihoon (Climate Services and Research Department, APEC Climate Center) ;
  • Cho, Jaepil (Climate Services and Research Department, APEC Climate Center)
  • Received : 2017.12.27
  • Accepted : 2018.09.14
  • Published : 2018.09.30
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Abstract

It is necessary to select the appropriate global climate model (GCM) to take into account the impacts of climate change on integrated water management. The objective of this study was to develop the selection technique of representative GCMs for uncertainty in climate change scenario. The selection technique which set priorities of GCMs consisted of two steps. First step was evaluating original GCMs by comparing with grid-based observational data for the past period. Second step was evaluating whether the statistical downscaled data reflect characteristics for the historical period. Spatial Disaggregation Quantile Delta Mapping (SDQDM), one of the statistical downscaling methods, was used for the downscaled data. The way of evaluating was using explanatory power, the stepwise ratio of the entire GCMs by Expert Team on Climate Change Detection and Indices (ETCCDI) basis. We used 26 GCMs based on CMIP5 data. The Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios were selected for this study. The period for evaluating reproducibility of historical period was 30 years from 1976 to 2005. Precipitation, maximum temperature, and minimum temperature were used as collected climate variables. As a result, we suggested representative 13 GCMs among 26 GCMs by using the selection technique developed in this research. Furthermore, this result can be utilized as a basic data for integrated water management.

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References

  1. Abatzoglou, J. T., and T. J. Brown, 2012. A comparison of statistical downscaling methods suited for wildfire applications. International Journal of Climatology 32(5): 772-780. doi:10.1002/joc.2312
  2. Baw, D. H., I. W. Jung, and D. P. Lettenmaier, 2011. Hydrologic uncertainty in climate change from IPCC AR4 GCM simulations of the Chungju basin, Korea. Journal of Hydrology 401(1-2): 90-105. doi:10.1016/j.jhdrol.2011.02.012
  3. Burger, G., T. Q. Murdock, A. T. Werner, and S. R. Sobie, 2012. Downscaling extremes-An intercomparison of multiple statistical methods for present climate. Journal of Climate 25: 4366-4388. doi:10.1175/JCLI-D-11-00408.1
  4. Cannon, A. J., S. R. Sobie, and T. Q. Murdock, 2015. Bias correction of GCM precipitation by quantile Mapping: How well do methods preserve changes in quantiles and extremes? Journal of Climate 28(17): 6938-6959. doi:10.1175/JCLI-D-14-00754.1
  5. Chang, H. J., and I. W. Jung, 2010. Spatial and temporal changes in runoff caused by climate change in a complex large river asdin in Oregon. Journal of Hydrology 388(3-4):186-207. doi:10.1016/j.jhydrol.2010.04.040
  6. Cho, J., S. Hwang, G. Go, K.-Y. Kim, and J. Kim, 2015. Assessing the climate change impacts on agricultural reservoirs using the SWAT model and CMIP5 GCMs. Journal of the Korean Society of Agricultural Engineers 57(5): 1-12 (in Korean). doi:10.5389/KSAE.2015.57.5.001
  7. Eum, H.-I., and A. J. Cannon, 2016a. Intercomparision of projected changes in climate extremes for south Korea: application of trend preserving statistical downscaling methods to the CMIP5 Ensemble. International Journal of Climatology 37(8): 3381-3397. doi:10.1002/joc.4924
  8. Eum, H.-I., A. J. Cannon, and T. Q. Murdock, 2016b. Intercomparison of multiple statistical downscaling methods: multi-criteria model selection for south Korea. Stochastic Environmental Research and Risk Assessment 31(3): 683-703. doi:10.1007/s00477-016-1312-9
  9. Hayhoe, K. A., 2010. A standardized framework for evaluating the skill of regional climate downscaling techniques. Ph.D. diss., Champaign, Illinois: University of Illinois at Urbana-Champaign.
  10. Hwang, S., K. Yoon, and C. L., 1981. Multiple attribute decision making: methods and applications. New York: Springer-Verlag.
  11. Hwang, S., and W. D. Graham, 2013a. Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation. Hydrology and Earth System Sciences 17(13): 4481-4502. doi:10.5194/hess-17-4481-2013
  12. Hwang, S., Y. G. Her, and S. Chang, 2013b. Uncertainty in regional climate change impact assessment using bias-correction technique for future climate scenarios. Journal of the Korean Society of Agricultural Engineers 55(4): 95-106 (in Korean). doi:10.5389/KSAE.2013.55.4.095
  13. Hwang, S., 2014. Assessing the performance of CMIP5 GCMs for various climatic elements and indicators over the southeast US. Journal of Koread Water Resources Association 47(11): 1039-1050 (in Korean). doi:10.3741/JKWRA.2014.47.11.1039
  14. Katsavounidis. I., C.-C. Jay Kuo, and Z. Zhang, 1994. A new initialization technique for generalized lloyd iteration. IEEE Signal Processing Letters 1(10): 144-146. doi:10.1109/97.329844
  15. Kim, C.-R., Y.-O. Kim, S. B. Seo, and S.-W. Choi, 2013. Water balance projection using climate change scenarios in the Korean Peninsula. Journal of Korea Water Resources Association 46(8): 807-819. doi:10.3741/JKWRA.2013.46.8.807
  16. Kim, J., J. Park, J.-H. Song, S. M. Jun, and M. S. Kang, 2016. Design flood estimation in the Hwangguji river watershed under climate and land use changes scenario. Journal of the Korean Society of Agricultural Engineers 58(1): 39-51 (in Korean). doi:10.5389/KSAE.2016.58.1.039
  17. Maurer, E. P., and H. G. Hidalgo, 2008. Utility of daily vs. monthly large-scale climate data: and intercomparison of two statistical downscaling methods. Hydrology and Earth System Sciences 12(2): 551-563. doi:10.5194/hess-12-551-2008
  18. Maurer, E. P., H. G. Hidalgo, T. Das, M. D. Dettinger, and D. R. Cayan, 2010. The utility of daily large-scale climate data in the assessmnet of climate change impact on daily streamflow in California. Hydrology and Earth System and Science 14: 1125-1138. doi:10.5194/hess-14-1125-2010
  19. Nam, W.-H., E.-M. Hong, and J.-Y. Choi, 2014. Uncertainty of water supply in agricultural reservoirs considering the climate change. Journal of the Korean Society of Agricultural Engineers 56(2): 11-23 (in Korean). doi:10.5389/KSAE.2014.56.2.011
  20. Nkomozepi, T. D., and S.-O. Chung, 2012. General circulation model derived climate change impact and uncertainty analysis of maize yield in Zimbabwe. Journal of the Korean Society of Agricultural Engineers 54(4): 83-92 (in Korean). doi:10.5389/KSAE.2012.54.4.083
  21. Park, J., J.-H. Kwon, T. Kim, and J.-H. Heo, 2014. Future inflow simulation considering the uncertainty of TFN model and GCMs on Chungju dam basin. Journal of Korea Water Resources Association 47(2): 135-143. doi:10.3741/JKWRA.2014.47.2.135
  22. Park, J., M. S. Kang, and I. Song, 2013. Flood risk assessment based on bias-corrected RCP scenarios with quantile mapping at a Si-Gum level. Journal of the Korean Society of Agricultural Engineers 55(4): 73-82 (in Korean). doi:10.5389/KSAE.2013.55.4.073
  23. Pirece, D. W., D. R. Cayan, E. P. Maurer, J. T. Abatzoglou, and K. C. Hegewisch, 2015. Inproved bias correction techniques for hydrological simulations of climate change. Journal of Hydrometorology 16(6): 2421-2442. doi:10.1175/JHM-D-14-0236.1
  24. Shon, K. H., D. H. Bae, and J. H. Ahn, 2014. Projection and analysis of drought according to future climate and hydrological information in Korea. Journal of Korea Water Resources Association 47(1): 71-82. doi:10.3741/JKWRA.2014.47.1.71
  25. Shon, T. S., S. D. Kim, and H. S. Shin, 2010. An analysis of the effect of climate change on flow in Nakdong river basin using watershed-based model. Journal of Korea Water Resources Association 43(10): 865-881. doi:10.3741/JKWRA.2010.43.10.865
  26. Temba, N., and S. O. Chung, 2014. Uncertainty ofhydro-meteorological predictions due to climate change inthe republic of Korea. Journal of Korea Water ResourcesAssociation 47(3): 257-267. doi:10.3741/JKWRA.2014.47.3.257
  27. Wood, A. W., L. R. Leung, V. Sridhar, and D. P. Lettenmaier, 2004. Hydrologic implication of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change 62(1-3): 189-216. doi:10.1023/B:CLIM.0000013685.99609.9e
  28. Yoo, S.-H., J.-Y. Choi, S.-H. Lee, Y.-G. Oh, and N.-Y. Park, 2012. The impacts of climate change of paddy water demand and unit duty of water using high-resolution climate scenarios. Journal of the Korean Society of Agricultural Engineers 54(2): 15-26 (in Korean). doi:10.5389/KSAE.2012.54.2.015
  29. Yun, D. K., S. O., Chung, and S. J. Kim, 2011. Climate change impacts on paddy water requirement. Journal of the Korean Society of Agricultural Engineers 53(4): 39-47 doi:10.5389/KSAE.2011.53.4.037
  30. Zhang, X., L. Alexander, G. C. Hegerl, P. Jones, A. K. Tank, T. C. Peterson, B. Trewin, and F. W. Zwiers, 2011. Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change 2(6): 851-870. doi:10.1002/wcc.147