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Uncertainty of Hydro-meteorological Predictions Due to Climate Change in the Republic of Korea

기후변화에 따른 우리나라 수문 기상학적 예측의 불확실성

  • Nkomozepi, Temba (Department of Agricultural Engineering, Kyungpook National University) ;
  • Chung, Sang-Ok (Department of Agricultural Engineering, Kyungpook National University)
  • Received : 2013.12.13
  • Accepted : 2014.02.05
  • Published : 2014.03.31

Abstract

The impact of the combination of changes in temperature and rainfall due to climate change on surface water resources is important in hydro-meteorological research. In this study, 4 hydro-meteorological (HM) models from the Rainfall Runoff Library in the Catchment Modeling Toolkit were used to model the impact of climate change on runoff in streams for 5 river basins in the Republic of Korea. Future projections from 2021 to 2040 (2030s), 2051 to 2070 (2060s) and 2081 to 2099 (2090s), were derived from 12 General Circulation Models (GCMs) and 3 representative concentration pathways (RCPs). GCM outputs were statistically adjusted and downscaled using Long-Ashton Research Station Weather Generator (LARS-WG) and the HM models were well calibrated and verified for the period from 1999 to 2009. The study showed that there is substantial spatial, temporal and HM uncertainty in the future runoff shown by the interquartile range, range and coefficient of variation. In summary, the aggregated runoff will increase in the future by 10~24%, 7~30% and 11~30% of the respective baseline runoff for the RCP2.6, RCP4.5 and RCP8.5, respectively. This study presents a method to model future stream-flow taking into account the HM model and climate based uncertainty.

기후변화에 따른 기온과 강수량의 변화가 지표수자원에 미치는 영향은 수문기상학 연구에서 매우 중요하다. 본 연구에서는 기후변화가 우리나라 5대강 유역의 유출량에 미치는 영향을 분석하기 위하여 Catchment Modeling Toolkit의 네가지 수문기상 모형을 사용하였다. 세 가지 RCP 시나리오에 대하여 12개 GCM 모형으로부터 미래 2021에서 2040까지(2030s), 2051에서 2070까지 (2060s) 및 2081에서 2099까지(2090s) 기간에 대한 기후자료를 추출하였다. 이들 자료는 LARS-WG 방법으로 상세화 하였으며, 수문기상 모형들은 1999부터 2009까지의 관측 자료를 이용하여 보정 및 검정하였다. 본 연구에서 미래의 유출량은 사분위 범위, 전체범위 및 변동계수 값이 시공간적으로 및 수문기상 모형에 따라서 큰 불확실성을 나타내었다. 종합적으로 볼 때 미래의 유출량은 기준년도에 비하여 RCP2.6, RCP4.5 및 RCP8.5 시나리오에 대하여 10~24%, 7~30% 및 11~30% 증가할 것으로 예상되었다. 본 연구는 수분기상모형과 기후변화 예측의 불확실성을 고려한 미래의 유출량을 모의할 수 있는 방법을 제시하였다.

Keywords

References

  1. Bae, D.H., Jung, I.W., and Chang, H. (2008). "Long term trend of rainfall and runoff in Korean river basins." Hydrological Processes, Vol. 22, No. 14, pp. 2644-2656. https://doi.org/10.1002/hyp.6861
  2. Bae, D.H., Jung, I.W., and Lettenmaier, D.P. (2011). "Hydrologic uncertainties in climate change from IPCC AR4 GCM simulations of the Chungju Basin, Korea." Journal of Hydrology, Vol. 401, No. 1, pp. 90-105. https://doi.org/10.1016/j.jhydrol.2011.02.012
  3. Boughton, W. (2004). "The Australian water balance model." Environmental Modelling & Software, Vol. 19, No. 10, pp. 943-956. https://doi.org/10.1016/j.envsoft.2003.10.007
  4. Burnash, R.J.C., Ferral, R.L., and McGuire, R.A. (1973). A Generalized StreamflowSimulation System-Conceptual Modelling for Digital Computers. US Department of Commerce, National Weather Service and State of California, Department of Water Resources, USA.
  5. Chen, H., Xiang, T., Zhou, X., and Xu, C.Y. (2012). "Impacts of climate change on the Qingjiang Watershed's runoff change trend in China." Stochastic Environmental Research and Risk Assessment, Vol. 26, pp. 847-858. https://doi.org/10.1007/s00477-011-0524-2
  6. Chiew, F.H.S., and Siriwardena, L. (2005). Estimation of SIMHYD parameter values for application in ungauged catchments. In: MODSIM 2005 International Congress on Modelling and Simulation, Melbourne, December 2005, pp. 2883- 2889.
  7. Chung, S.O. (2013). "Projecting future paddy irrigation demands in Korea." Irrigation and Drainage, Vol. 62, pp. 297-305. https://doi.org/10.1002/ird.1711
  8. Eum, H.I., Simonovic, S.P., and Kim, Y.O. (2010). "Climate change impact assessment using k-nearest neighbor weather generator: case study of the Nakdong River basin in Korea." Journal of Hydrologic Engineering, Vol. 15, No. 10, pp. 772-785. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000251
  9. Gardner, L.R. (2009). "Assessing the effect of climate change on mean annual runoff." Journal of Hydrology, Vol. 379, pp. 351-359. https://doi.org/10.1016/j.jhydrol.2009.10.021
  10. Jeong, H.-G., Kim, S.-J., and Ha, R. (2013). "Assessment of climate change impact on storage behavior of chungju and regulation dams using SWAT Model." Journal of Korea Water Resources Association, Vol. 46, pp. 1235 -1247. https://doi.org/10.3741/JKWRA.2013.46.12.1235
  11. Kim, C.-R., Kim, Y.-O, Seo, S.-B., and Choi, S.-W. (2013b). "Water balance projection using climate scenarios in the Korean Peninsula." Journal of Korea Water Resources Association, Vol. 46, pp. 807-809. (in Korean) https://doi.org/10.3741/JKWRA.2013.46.8.807
  12. Kim, J., Choi, J., Choi, C., and Park, S. (2013a). "Impacts of changes in climate and land use/land cover under IPCC RCP scenarios on streamflow in the Hoeya River Basin, Korea." Science of the Total Environment, Vol. 452, pp. 181-195.
  13. Kim, Y.O., Jeong, D., and Ko, I.H. (2006). "Combining rainfall-runoff model outputs for improving ensemble streamflow prediction." Journal of Hydrologic Engineering, Vol. 11, No. 6, pp. 578-588. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:6(578)
  14. Kling, H., Fuchs, M., and Paulin, M. (2012). "Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios." Journal of Hydrology, Vol. 424-425, pp. 264-277. https://doi.org/10.1016/j.jhydrol.2012.01.011
  15. Krause, P., Boyle, D.P., and Base, F. (2005). "Comparison of different efficiency criteria for hydrological model assessment." Advances in Geosciences, Vol. 5, No. 5, pp. 89-97. https://doi.org/10.5194/adgeo-5-89-2005
  16. Lee, S.J., Maeng, S.J., Kim, H.S., and Na, S.I., (2012). "Analysis of runoff in the Han River basin by SSARR model considering agricultural water." Paddy and Water Environment, Vol. 10, No. 4, pp. 265-280. https://doi.org/10.1007/s10333-011-0278-y
  17. Lupia, F., 2013. ETo-PM version 0.9. Available on the internet. URL http://dspace.inea.it.
  18. Moss, R.H., et al. (2010). "The next generation of scenarios for climate change research and assessment." Nature, Vol. 463, No. 7282, pp. 747-756. https://doi.org/10.1038/nature08823
  19. Nester, T., Kirnbauer, R., Gutknecht, D., and Bloschl, G. (2011). "Climate and catchment controls on the performance of regional flood simulations." Journal of Hydrology, Vol. 402, No. 3, pp. 340-356. https://doi.org/10.1016/j.jhydrol.2011.03.028
  20. Nkomozepi, T., and Chung, S.-O. (2014). "The effects of climate change on the water resources of the Geumho River Basin, Republic of Korea." Journal of Hydro-environment Research, pp. 1-9. (in press)
  21. Oh, B.-H. (2013). 2012 Modularization of Korea's Development Experience: Korea's River Basin Management Policy. Ministry of Strategy and Finance, Sejong, Republic of Korea. 88pp.
  22. Podger, G. (2004). RRL Rainfall Runoff Library users guide. Cooperative Research Centre for Catchment Hydrology. Victoria. Australia.
  23. Rivarola Sosa, J.M., Brandani, G., Dibari, C., Moriondo, M., Ferrise, R., Trombi, G., and Bindi, M. (2011). Climate change impact on the hydrological balance of the Itaipu Basin. Meteorological Applications, Vol. 18, No. 2, pp. 163-170. https://doi.org/10.1002/met.213
  24. Schreider, S.Y., Jakeman, A.J., Letcher, R.A., Nathan, R.J., Neal, B.P., and Beavis, S.G. (2002). Detecting changes in streamflow response to changes in nonclimatic catchment conditions: farm dam development in the Murray-Darling basin, Australia. Journal of Hydrology, Vol. 262, No. 1, pp. 84-98. https://doi.org/10.1016/S0022-1694(02)00023-9
  25. Shi, C., Zhou, Y., Fan, X., and Shao, W. (2013). A study on the annual runoff change and its relationship with water and soil conservation practices and climate change in the middle Yellow River basin. Catena, Vol. 100, pp. 31-41. https://doi.org/10.1016/j.catena.2012.08.007
  26. Sohn, K.-H., Bae, D.-H., and Ahn, J.-H. (2014). Projection and analysis of drought according to future climate and hydrological information in Korea. Journal of Korea Water Resources Association, Vol. 47, pp. 71-82. (in Korean) https://doi.org/10.3741/JKWRA.2014.47.1.71
  27. Van Vuuren, D.P. (2011). The representative concentration pathways: an overview. Climate Change, Vol. 109, pp. 5-31. https://doi.org/10.1007/s10584-011-0148-z
  28. Vaze, J., Post, D.A., Chiew, F.H.S., Perraud, J.M., Teng, J., and Viney, N.R. (2011). Conceptual rainfall-runoff model performance with different spatial rainfall inputs. Journal of Hydrometeorology, Vol. 12, No. 5, pp. 1100-1112. https://doi.org/10.1175/2011JHM1340.1
  29. Velazquez, J.A., et al. (2013). An ensemble approach to assess hydrological models' contribution to uncertainties in the analysis of climate change impact on water resources. Hydrological and Earth System Sciences, Vol. 17, pp. 565-578. https://doi.org/10.5194/hess-17-565-2013
  30. Zhang, X.-C., Liu, W.-Z., and Chen, J. (2011). Trend and uncertainty analysis of simulated change impacts with multiple GCMs and emission scenarios. Agricultural and Forest Meteorology, Vol. 151, pp. 1297-1304. https://doi.org/10.1016/j.agrformet.2011.05.010

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