Korean Journal of Agricultural Science (농업과학연구)

Institute of Agricultural Science, CNU (충남대학교 농업과학연구소)
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Analyzing on the cause of downstream submergence damages in rural areas with dam discharge using dam management data

  • Sung-Wook Yun (Agricultural Researcher, Department of Agricultural Engineering, National Institute of Agricultural Sciences) ;
  • Chan Yu (Department of Agricultural Engineering, Institute of Agricultural and Life Science, Gyeongsang National University)
  • Received : 2023.04.24
  • Accepted : 2023.06.20
  • Published : 2023.09.01
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Abstract

The downstream submergence damages caused during the flood season in 2020, around the Yongdam-dam and five other sites, were analyzed using related dam management data. Hourly- and daily-data were collected from public national websites and to conduct various analyses, such as autocorrelation, partial-correlation, stationary test, trend test, Granger causality, Rescaled analysis, and principal statistical analysis, to find the cause of the catastrophic damages in 2020. The damage surrounding the Yongdam-dam in 2020 was confirmed to be caused by mis-management of the flood season water level. A similar pattern was found downstream of the Namgang- and Hapcheon-dams, however the damage caused via discharges from these dams in same year is uncertain. Conversely, a different pattern from that of the Yongdam-dam was seen in the areas downstream of Sumjingang- and Daecheongdams, in which the management of the flood season water level appeared appropriate and hence, the damages is assumed to have occurred via the increase in the absolute discharge amount from the dams and flood control capacity leakage of the downstream river. Because of the non-stationarity of the management data, we adapted the wavelet transform analysis to observe the behaviors of the dam management data in detail. Based on the results, an increasing trend in the discharge amount was observed from the dams after the year 2000, which may serve as a warning about similar trends in the future. Therefore, additional and continuous research on downstream safety against dam discharges is necessary.

Keywords

Acknowledgement

본 연구는 한국연구재단의 재원으로 학문균형발전사업 지역대학우수과학자지원사업의 지원을 받아 연구되었음(과제번호:2019R1I1A3A01041192).

References

  1. BAIK (Board of audit and inspection of Korea). 2022. Investigation on real condition of dam downstream region against flooding, Audit report, Special issue. 2022. 12. BAIK, Seoul, Korea. [in Korean]
  2. Beran J. 1992. Statistical methods for data with long-range dependence. Statistical Science 7:404-416.
  3. Boulange, J, Hanasaki N, Yamazaki D, Pokhrel Y. 2021. Role of dams in reducing global flood exposure under climate change. Nature Communications 12:417:1-7. https://doi.org/10.1038/s41467-020-20314-w
  4. Chua G. 2020. Chapter 7: Cross-correlations, fourier transform, and wavelet transform. Time series analysis handbook. Time series analysis handbook. Accessed in https://phdinds-aim.github.io/time_series_handbook/ on 20 Feburary 2023..
  5. Dickey DA. 2016. Stationarity issues in time series models. Statistics and Data Analysis Paper 192-30.
  6. Granger CWJ. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424-438. https://doi.org/10.2307/1912791
  7. Granger CWJ, Joyeux R. 1980. An Introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis 1:15-29. https://doi.org/10.1111/j.1467-9892.1980.tb00297.x
  8. Graves T, Gramacy RB, Watkins N, Franzke C. 2017. A brief history of long memory: Hurst, Mandelbrot and the road to ARFIMA, 1951-1980. Entropy 19:437. DOI:10.3390/e19090437.
  9. Grinsted A, Moore JC, Jevrejeva S. 2004. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics 11:561-566. https://doi.org/10.5194/npg-11-561-2004
  10. Gujarati DN, Porter DC. 2008. Basic econometrics, 5th ed. McGraw-Hill Education, New York, USA. 
  11. Han MH. 2022. Newspaper of YeonhapNews. The deepening climate crisis...Record heat waves, floods, and droughts continue. Accessed in https://m.yonhapnewstv.co.kr/news/MYH20221231007400032 on 21 December 2022. [in Korean]
  12. Hosking JRM. 1981. Fractional differencing. Biometrika 68:165-176. https://doi.org/10.1093/biomet/68.1.165
  13. Huh KW. 2022. Newpapers of Namdo-ilbo. Gurye-gun completed the process of accepting land for flood recovery in Seomjingang River. Accessed in http://www.namdonews.com/news/articleView.html?idxno=691857 on 16 August 2022. [in Korean]
  14. Hussain MM, Mahmud I. 2019. pyMannKendall: A python package for non parametric Mann Kendall family of trend tests. Journal of Open Source Software 4:1556. DOI:10.21105/joss.01556.
  15. International Rivers. 2011. Wrong climate for big dams: Fact sheet,-destroying rivers will worsen climate crisis. Accessed in https://archive.internationalrivers.org/resources/wrong-climate-for-big-dams-fact-sheet3373 on 29 August 2022.
  16. Kang BS, Lee SJ, Kang DH, Kim YO. 2007. A flood risk projection for Yongdam dam against future climate change. Journal of Hydro-environment Research 1:118-125. [in Korean] https://doi.org/10.1016/j.jher.2007.07.003
  17. Kim CH. 2022. Floods in Kentucky kill at least 19 people. Newspaper of Hankook-ilbo. Accessed in https://www.hankookilbo.com/News/Read/A2022073014070004327?did=GO on 30 July 2022. [in Korean]
  18. Kim JW, Cho SS, Yeo IK. 2009. A fast Bayesian detection of change points in long-memory processes. Korean Journal of Applied Statistics 22:735-744. [in Korean] https://doi.org/10.5351/KJAS.2009.22.4.735
  19. Kisi O. 2009. Neural networks and wavelet conjunction model for intermittent stream flow forecasting. Journal of Hydrologic Engineering 14:773-782. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000053
  20. Krieger S, Freij N, Brazhe A, Torrence C, Compo GP, Contributors. 2017. PyCWT. Accessed in https://github.com/regeirk/pycwt on 1 January.
  21. Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y. 1992. Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics 54:159-178. https://doi.org/10.1016/0304-4076(92)90104-Y
  22. Kwon HH, Moon YI. 2005. Analysis of hydrologic time series using wavelet transform. Journal of Korea Water Resources Association 38:439-448. [in Korean] https://doi.org/10.3741/JKWRA.2005.38.6.439
  23. K-water. 2023. Yongdam Dam Branch. Accessed in https://www.kwater.or.kr/water/sub01/sub05/20060557/works.do?brdId=KO27&s_officecode=10940029&s_flag=1&s_mid=909 on 23 Feburary 2023. [in Korean]
  24. Labat D. 2005. Recent advances in wavelet analyses: Part 1. A review of concepts, Journal of Hydrology 314:275-288. https://doi.org/10.1016/j.jhydrol.2005.04.003
  25. Labat D, Ronchail J, Guyot JL. 2005. Recent advances in wavelet analyses: Part 2. Amazon, Parana, Orinoco and Congo discharges time scale variability, Journal of Hydrology 314:289-311. https://doi.org/10.1016/j.jhydrol.2005.04.004
  26. Lee BS. 2022. Newspaper of YeonhapNews. China Sees Flood Damage, World's Largest Dam Dangerous. Accessed in https://www.yonhapnewstv.co.kr/news/MYH20200720017400038 on 28 July 2022. [in Korean]
  27. Levene H. 1960. In Contributions to probability and statistics: Essays in Honor of Harold Hotelling. pp. 278-292. Stanford University Press, London, UK.
  28. Mandelbrot BB, van Ness JW. 1968. Fractional Brownian motions, Fractional noises and applications. SIAM Review 10:422-437. https://doi.org/10.1137/1010093
  29. Mandelbrot BB, Wallis JR. 1968. Noah, Joseph, and operational hydrology. Water Resources Research 4:909-918. https://doi.org/10.1029/WR004i005p00909
  30. Mandelbrot BB, Wallis JR. 1969. Computer experiments with fractional Gaussian noises: Part 3, mathematical appendix. Water Resources Research 5:260-267. https://doi.org/10.1029/WR005i001p00260
  31. Mezosi G. 2022. Natural hazards and the mitigation of their impact. p. 138. Springer, Cham, Switzerland.
  32. MoE (Ministry of Environment). 2021. Report on Investigation results and emergency action plan of dam downstream damage on August, 2000. 2021. 8. 3. MoE, Sejong, Korea. [in Korean]
  33. MOLIT (Ministry of Land, Infrastructure and Transportation). 2015. Regulation for dam management. MOLIT, Sejong, Korea. [in Korean]
  34. Nakamura R, Shimatani Y. 2021. Extreme-flood control operation of dam in Japan. Journal of Hydrology: Regional Studies 35:100821:1-12.
  35. Park JJ, Park KB, Choi JK, Chang IS. 2018. Analysis of flood season dam operation variation by initial condition. Journal of Water Treatment 26:13-21. [in Korean]
  36. Prahlada R, Deka PC. 2015. Forecasting of time series significant wave height using wavelet decomposed neural network. Aquatic Procedia 4:540-547. https://doi.org/10.1016/j.aqpro.2015.02.070
  37. Predybaylo E. 2014. WAVETEST example python script for WAVELET. Accessed in https://paos.colorado.edu/research/wavelets/wave_python/waveletAnalysis.py on 16 December 2022.
  38. Serinaldi F, Kilsby CG. 2015. Stationarity is undead: Uncertainty dominates the distribution of extremes. Advances in Water Resources 77:17-36 https://doi.org/10.1016/j.advwatres.2014.12.013
  39. Torrence C, Compo GP. 1998. A practical guide to wavelet analysis. Bulletin of the American Meteorological Society 79:61-78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
  40. USGS (United State Geology Survey). 2018. The 100-year flood. Accessed in https://www.usgs.gov/special-topics/water-science-school/science/100-year-flood on 10 April 2023.
  41. Yu DY. 2023. Newspaper of Nocut-news. Board of Audit and Inspection of Korea "Lack of measures to prevent recurrence of flood damage areas in Seomjingang River". Accessed in https://www.nocutnews.co.kr/news/5880125 on 16 January 2023. [in Korean]
  42. Zhou HC, Peng Y, Liang GH. 2008. The research of monthly discharge predictor corrector model based on wavelet decomposition. Water Resource Management 22:217-227. https://doi.org/10.1007/s11269-006-9152-x