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Assessment and Improvement of Monthly Coefficients of Kajiyama Formular on Climate Change

기후변화에 따른 가지야마 공식 월별 보정계수 개선 및 평가

  • Seo, Jiho (Taeseong Construction Co., Ltd.) ;
  • Lee, Dongjun (Department of Regional Infrastructure Engineering, Kangwon National University) ;
  • Lee, Gwanjae (Department of Regional Infrastructure Engineering, Kangwon National University) ;
  • Kim, Jonggun (Institute of Agricultural and Life Science, Kangwon National University) ;
  • Kim, Ki-sung (Department of Regional Infrastructure Engineering, Kangwon National University) ;
  • Lim, Kyoung Jae (Department of Regional Infrastructure Engineering, Kangwon National University)
  • Received : 2018.06.19
  • Accepted : 2018.08.23
  • Published : 2018.09.30

Abstract

The Kajiyama formula, which is an empirical formula based on the maximum flood data at Korean watersheds, has been widely used for the design of hydraulic structures and management of watersheds. However, this formula was developed based on meteorological data and flow measured during early 1900s so that it could not consider the recently changed rainfall pattern due to climate changes. Moreover, the formula does not provide the monthly coefficients for 5 months including July and August (flood season), which causes the uncertainty to accurately interpret runoff characteristics at a watershed. Thus, the objective of this study is to enhance the monthly coefficients based on the recent meteorological data and flow data expanding the range of rainfall classification. The simulated runoff using the enhanced monthly coefficients showed better performance compared to that using the original coefficients. In addition, we evaluated the applicability of the enhanced monthly coefficient for future runoff prediction. Based on the results of this study, we found that the Kajiyame formula with the enhanced coefficients could be applied for the future prediction. Hence, the Kajiyama formula with enhanced monthly coefficient can be useful to support the policy and plan related to management of watersheds in Korea.

Keywords

References

  1. Ahn, S. R., Y. J. Lee, G. A. Park, and S. J. Kim, 2008. Analysis of future land use and climate change impact on stream discharge. Journal of Civil Engineering 28(2B): 215-224 (in Korea).
  2. Arnold, J. G., 1992. Spatial scale variability in model development and parameterization, ph.D, Dissertation, Purdue University, West Lafayette, IN.
  3. Arnold, J. G., R. Srinivasan, R. S. Muttiah, and J. R. Williams, 1998. Large area hydrologic modeling and assessment: part I: model development. Journal of the American Water Resources Association 34(1): 73-89 (in United States). doi:10.1111/j.1752-1688.1998.tb05961.x.
  4. Box, G. E., and G. C. Tiao, 2011. Bayesian inference in statistical analysis 40. doi:10.1002/9781118033197.
  5. Duan, Q., 2003. Global optimization for watershed model calibration. in Calibration of Watershed Models 89-104. doi:10.1002/9781118665671.ch6.
  6. Griensven, A. V., and T. Meixner, 2007. A global and efficient multi-objective auto-calibration and uncertainty estimation method for water quality catchment models. Journal of Hydroinformatics 9(4): 277-291 (in United Kingdom). doi:10.2166/hydro.2007.104.
  7. 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. doi:10.5194/hess-16-3383-2012.
  8. Han, W. S., W. B. Sim, B. J. Lee, and J. H. Yoo, 2012. The proposal of evaluation method for local government infrastructure vulnerability relating to climate change driven flood. Journal of Climate Change Research 3(1): 25-37 (in Korea).
  9. Holland, J. H., 1975. Adaptation in natural and artificial systems. Michigan, Ind.: University of Michigan Press 183 p. 975.
  10. Hwang, C. S., C. U. Choi, and J. S. Choi, 2014. Impact of IPCC RCP scenarios on streamflow and sediment in the Hoeya river basin. Journal of the Korean society for geo-spatial information system 22(3): 11-19 (in Korea). doi:10.7319/kogsis.2014.22.
  11. Jang, D. W., B. S. Kim, and J. H. Kim, 2012. The quantification of disaster impact of extreme rainfall under climate change in Korea. Journal of the Korean Society of Hazard Mitigation 12(4): 169-178 (in Korea). doi:10.9798/KOSHAM.2012.12.4.169.
  12. Jang, J. S., 2003. Introduction of hydrologic models and parameters. Journal of Korean National Committee on Irrigation and Drainage 10(1): 95-102 (in Korea).
  13. Jo, J. P., I. W. Jung, J. Y. Lee, J. P. Kim, S. K. Yoon, and E. J. Lee, 2013. Analysis of CMIP5 data in the water resources considering uncertainty, Korea Water Resources Association (KWRA).
  14. Jung, B. H., B. C. Koo, I. Y. Jung, C. K. Park, G. S. Park, D. S. Oa, S. G. Shin, Y. K. Jo, M. W. Kim, and Y. J. Park, 2004. Improving the hydraulic and structural safety of reservoir spillways for flood, 14-35. Rural Research Institute (in Korea).
  15. Kajiyama, A., 1928. Flood forecasting for Han, Nakdong, and Daedong Rivers in Korea. Journal of Japan Society of Civil Engineers, Japan.
  16. Kim, H. N., E. R. Lee, S. U. Kang, and H. G. Choi, 2015. Long-term natural flow prediction based on RCP climate change scenarios in Geumho river watershed. Journal of Korean Review of Crisis and Emergency Management 11(5): 151-166 (in Korea). https://doi.org/10.14251/krcem.2015.11.10.151
  17. Kim, K. C., M. H. Shin, Y. H. Choi, J. Y. Seo, and J. D. Choi, 2008. Comparison of water resources by Kajiyama and SWAT models for an ungauged small watershed. Proceedings of the 2008 Korea Water Resources Association Conference, Korea Water Resources Association, 2244-2248 (in Korea).
  18. Lee, J. W., N. W. Kim, J. W. Lee, and B. H. Seo, 2009. Estimation of runoff curve number for ungauged watershed using SWAT. Journal of the Korean Society of Agricultural Engineers 51(6): 11-16 (in Korea). doi:10.5389/KSAE.2009.51.6.011.
  19. Nash, J. E., and J. V. Sutcliffe, 1970. River flow forecasting through conceptual models part I - A discussion of principal. Journal of Hydrology 10(3): 282-290. doi:10.1016/0022-1694(70)90255-6.
  20. National Institute of Meteorological Research, 2011. Climate change scenario report 2011 to respond to the IPCC 5th Assessment Report, 11-1360395-000233-01, NIMR.
  21. Neitsch, S. L., J. G. Arnold, J. R. Kiniry, and J. R. Williams, 2005. Soil and Water Assessment Tool Theoretical Documentation Version 2005, USDA, ARS, Temple, Texas, 1-647.
  22. Nelder, J. A., and R. A. Mead, 1965. Simplex method for function minimization. Computer Journal 7: 308-313. https://doi.org/10.1093/comjnl/7.4.308
  23. Noh, J. K., 1999. Generalization of Kajiyama formula for estimating monthly discharges. Proceedings of the 1999 Korea Water Resources Association Conference, Korea Water Resources Association, 221-226 (in Korea).
  24. Shin, Y. C., M. H. Shin, W. K. Kim, K. J. Lim, and J. D. Choi, 2007. Estimation of streamflow discharges using Kajiyama equation and SWAT Model. Journal of Korean National Committee on Irrigation and Drainage 14(1): 41-49 (in Korea).
  25. Stocker, T. F., D. Qin, G. K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley, 2013. Climate Change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, IPCC, Cambridge University Press, United Kingdom, 1535.
  26. Park, J. Y., H. Jung, C. H. Jang, and S. J. Kim, 2014. Assessing climate change impact on hydrological components of Yongdam dam watershed using RCP emission scenarios and SWAT Model. Journal of the Korean Society of Agricultural Engineers 56(3): 19-29 (in Korean). doi:10.5389/KSAE.2014.56.3.019.
  27. Ra, Y. H., D. S. Yoo, J. G. Kim, Y. S. Park, Y. C. Park, S. G. Heo, K. S. Kim, J. D. Choi, and K. J. Lim, 2007. Analysis of flow-resources using the SWAT model in the YeongWol watershed. Journal of Agriculture and Life Sciences Research Institute 18: 147-154 (in Korea).