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A decision-centric impact assessment of operational performance of the Yongdam Dam, South Korea

용담댐 기존운영에 대한 의사결정중심 기후변화 영향 평가

  • Kim, Daeha (Department of Civil Engineering, Jeonbuk National University) ;
  • Kim, Eunhee (Department of Civil Engineering, Jeonbuk National University) ;
  • Lee, Seung Cheol (Department of Civil Engineering, Jeonbuk National University) ;
  • Kim, Eunji (Department of Civil Engineering, Jeonbuk National University) ;
  • Shin, June (Department of Civil Engineering, Jeonbuk National University)
  • 김대하 (전북대학교 토목환경자원에너지공학부) ;
  • 김은희 (전북대학교 토목환경자원에너지공학부) ;
  • 이승철 (전북대학교 토목환경자원에너지공학부) ;
  • 김은지 (전북대학교 토목환경자원에너지공학부) ;
  • 신준 (전북대학교 토목환경자원에너지공학부)
  • Received : 2021.12.21
  • Accepted : 2022.02.03
  • Published : 2022.03.31

Abstract

Amidst the global climate crisis, dam operation policies formulated under the stationary climate assumption could lead to unsatisfactory water management. In this work, we assessed status-quo performance of the Yongdam Dam in Korea under various climatic stresses in flood risk reduction and water supply reliability for 2021-2040. To this end, we employed a decision-centric framework equipped with a stochastic weather generator, a conceptual streamflow model, and a machine-learning reservoir operation rule. By imposing 294 climate perturbations to dam release simulations, we found that the current operation rule of the Yongdam dam could redundantly secure water storage, while inefficiently enhancing the supply reliability. On the other hand, flood risks were likely to increase substantially due to rising mean and variability of daily precipitation. Here, we argue that the current operation rules of the Yongdam Dam seem to be overly focused on securing water storage, and thus need to be adjusted to efficiently improve supply reliability and reduce flood risks in downstream areas.

대기온실가스 증가로 인해 전지구 평균기온은 이미 1.0℃ 이상 상승했고 폭염, 가뭄, 홍수 등 극한 기상현상의 빈도는 점점 더 높아질 것으로 전망되고 있다. 본 연구에서는 전북·충청지역의 이·치수안전도 확보에 큰 역할을 하고 있는 용담댐의 기존 운영방식이 기후변화에 얼마나 취약한 지 의사결정 지표를 중심으로 평가하였다. 현실적인 기후 스트레스 테스트를 위해 GR6J 강우-유출 모형, Random Forests 댐운영 모형을 관측자료에 적합시켰고 추계학적 기법으로 생성된 294개의 기후스트레스 시계열을 모형에 입력해 연최대일방류량, 저수량신뢰도, 공급신뢰도의 변화를 분석하였다. 그 결과 2021~2040년 기간 용담댐 저수량신뢰도는 과도한 수준으로 증가할 것으로 전망되었고 이에 반해 공급신뢰도의 증가는 저수량 신뢰도에 미치지 못할 것으로 나타났다. 평균강수량과 강수변동성의 증가로 20년 빈도 연최대방류량은 50%의 확률로 43% 증가할 것으로 나타났다. 용담댐의 기존운영방식은 저수량 확보에 과도하게 치중되어 있는 것으로 판단되며 이 운영이 지속될 경우 용담댐 하류지역의 홍수위험은 더 가중될 것으로 예상된다.

Keywords

Acknowledgement

본 연구는 2021년도 전북녹색환경지원센터의 연구사업비 지원을 받아 수행되었습니다(Project No. 21-14-01-07-36). 이에 감사드립니다.

References

  1. Apipattanavis, S., Podesta, G., Rajagopalan, B., and Katz, R.W. (2007). "A semiparametric multivariate and multisite weather generator." Water Resources Research, Vol. 43, W11401. https://doi.org/10.1029/2006WR005714
  2. Biemans, H., Haddeland, I., Kabat, P., Ludwig, F., Hutjes, R.W.A., Heinke, J., von Bloh, W., and Gerten, D. (2011). "Impact of reservoirs on river discharge and irrigation water supply during the 20th century." Water Resources Research, Vol. 47, W03509. https://doi.org/10.1029/2009WR008929
  3. Breda, J.P.L.F., de Paiva, R.C.D., Collischon, W., Bravo, J.M., Siqueira, V.A., and Steinke, E.B. (2020). "Climate change impacts on South American water balance from a continental-scale hydrological model driven by CMIP5 projections." Climatic Change, Vol. 159, pp. 503-522. https://doi.org/10.1007/s10584-020-02667-9
  4. Breiman, L. (2001). "Random forests." Machine Learning, Vol. 45, pp. 5-32. https://doi.org/10.1023/A:1010933404324
  5. Breiman, L., Friedman, J., Olshen, R., Stone, C., Steinberg, D., and Colla, P. (1984). CART: Classification and regression trees. Routledge, New York, NY, U.S.
  6. Brown, C., and Wilby, R.L. (2012). "An alternate approach to assessing climate risks." Eos Transactions American Geophysical Union, Vol. 93, pp. 401-402. https://doi.org/10.1029/2012EO410001
  7. Brown, C., Ghile, Y., Laverty, M., and Li, K. (2012). "Decision scaling: Linking bottom-up vulnerability analysis with climate projections in the water sector." Water Resources Research, Vol. 48, W09537.
  8. Brutsaert, W. (2017). "Global land surface evaporation trend during the past half century: Corroboration by Clausius-Clapeyron scaling." Advances in Water Resources, Vol. 106, pp. 3-5. https://doi.org/10.1016/j.advwatres.2016.08.014
  9. Burger, G, Sobie, S.R., Cannon, A.J., Werner, A.T., and Murdock, T.Q. (2013). "Downscaling extremes: An intercomparison of multiple meth-ods for future climate." Journal of Climate, Vol. 26, pp. 3429-3449. https://doi.org/10.1175/JCLI-D-12-00249.1
  10. Dai, A. (2013). "Increasing drought under global warming in observations and models." Nature Climate Change, Vol. 3, pp. 52-58. https://doi.org/10.1038/nclimate1633
  11. Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J., and Pasteris, P.P. (2008). "Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States." International Journal of Climatology, Vol. 28, pp. 2031-2064. https://doi.org/10.1002/joc.1688
  12. Ehsani, N., Vorosmarty, C.J., Fekete, B.M., and Stakhiv, E.Z. (2017). "Reservoir operations under climate change: Storage capacity options to mitigate risk." Journal of Hydrology, Vol. 555, pp. 435-446. https://doi.org/10.1016/j.jhydrol.2017.09.008
  13. Emami, K. (2020). "Adaptive flood risk management. Irrigation and Drainage." Vol. 69, No. 2, pp. 230-242. https://doi.org/10.1002/ird.2411
  14. Foley, M.M., Bellmore, J.R., O'Connor, J.E., Duda, J.J., East, A.E., Grant, G.E., Anderson, C.W., Bountry, J.A., Collins, M.J., Connolly, P.J., and Craig, L.S. (2017). "Dam removal: Listening in." Water Resources Research, Vol. 53, pp. 5229-5246. https://doi.org/10.1002/2017WR020457
  15. Georgakakos, A.P., Yao, H., Kistenmacher, M., Georgakakos, K.P., Graham, N.E., Cheng, F.-Y., Spencer, C., and Shamir, E. (2012). "Value of adaptive water resources management in Northern California under climatic variability and change: Reservoir management." Journal of Hydrology, Vol. 412-413, pp. 34-46. https://doi.org/10.1016/j.jhydrol.2011.04.038
  16. Gupta, H.V., Kling, H., Yilmaz, K.K., and Martinez, G.F. (2009). "Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling." Journal of Hydrology, Vol. 377, pp. 80-91. https://doi.org/10.1016/j.jhydrol.2009.08.003
  17. Hashimoto, T., Stedinger, J.R., and Loucks, D.P. (1982). "Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation." Water Resources Research, Vol. 18, pp. 14-20. https://doi.org/10.1029/WR018i001p00014
  18. Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., Kim, H., and Kanae, S. (2013). "Global flood risk under climate change." Nature Climate Change, Vol. 3, pp. 816-821. https://doi.org/10.1038/nclimate1911
  19. Intergovernmental Panel on Climate Change (IPCC) (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, Edited by Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.M., Cambridge University Press, Cambridge, UK and New York, NY, U.S.
  20. Intergovernmental Panel on Climate Change (IPCC) (2021). Summary for Policymakers. In: Climate Change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Edited by Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Pean, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J.B.R., Maycock, T.K., Waterfield, T., Yelekci, O., Yu, R., and Zhou, B., In Press.
  21. Jeong, Y., and Eum, H.-I. (2015). "Application of a statistical interpolation method to correct extreme values in high-resolution gridded climate variables." Journal of Climate Chnage Research, Vol. 6, pp. 331-334. https://doi.org/10.15531/ksccr.2015.6.4.331
  22. Kim, D., and Chun, J.A. (2021). "Revisiting a two-parameter Budyko equation with the complementary evaporation principle for proper consideration of surface energy balance." Water Resources Research, Vol. 57, e2021WR030838.
  23. Kim, D., Chun, J.A., and Aikins, C.M. (2018). "An hourly-scale scenario-neutral flood risk assessment in a mesoscale catchment under climate change." Hydrological Processes, Vol. 32, pp. 3416-3430. https://doi.org/10.1002/hyp.13273
  24. Kim, D., Chun, J.A., and Choi, S.J. (2019). "Incorporating the logistic regression into a decision-centric assessment of climate change impacts on a complex river system." Hydrology and Earth System Sciences, Vol. 23, pp. 1145-1162. https://doi.org/10.5194/hess-23-1145-2019
  25. Knighton, J., Steinschneider, S., and Walter, M.T. (2017). "A vulnerability-based, bottom-up assessment of future riverine flood risk using a modified peaks-over-threshold approach and a physically based hydrologic model." Water Resources Research, Vol. 53, pp. 10043-10064. https://doi.org/10.1002/2017WR021036
  26. K-water (2019). The handbook of water management in practice.
  27. Kwon, H.H., Lall, U., and Khalil, A.F. (2007). "Stochastic simulation model for nonstationary time series using an autoregressive wavelet decomposition: Applications to rainfall and temperature." Water Resources Research, Vol. 43, W05407. https://doi.org/10.1029/2006WR005258
  28. Meinshausen, M., Nicholls, Z.R., Lewis, J., Gidden, M.J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., and Canadell, J.G. (2020). "The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500." Geoscientific Model Development, Vol. 13, pp. 3571-3605. https://doi.org/10.5194/gmd-13-3571-2020
  29. Mok, J.Y., Choi, J.H., and Moon, Y.I. (2020). "Prediction of multipurpose dam inflow using deep learning." Journal of Korea Water Resources Association, Vol. 53, pp. 97-105. https://doi.org/10.3741/JKWRA.2020.53.2.97
  30. Nilsson, C., Reidy, C.A., Dynesius, M., and Revenga, C. (2005). "Fragmentation and flow regulation of the world's large river systems." Science, Vol. 308, pp. 405-408. https://doi.org/10.1126/science.1107887
  31. Penman, H.L. (1948). "Natural evaporation from open water, bare soil and grass." Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, Vol. 193, No. 1032, pp. 120-145.
  32. Perrin, C., Michel, C., and Andreassian, V. (2003). "Improvement of a parsimonious model for streamflow simulation." Journal of Hydrology, Vol. 279, pp. 275-289. https://doi.org/10.1016/S0022-1694(03)00225-7
  33. Poff, N.L., Brown, C.M., Grantham, T.E., Matthews, J.H., Palmer, M.A., Spence, C.M., Wilby, R.L., Haasnoot, M., Mendoza, G.F., Dominique, K.C., and Baeza, A. (2016). "Sustainable water management under future uncertainty with eco-engineering decision scaling." Nature Climate Change, Vol. 6, pp. 25-34. https://doi.org/10.1038/nclimate2765
  34. Poncelet, C., Merz, R., Merz, B., Parajka, J., Oudin, L., Andreassian, V., and Perrin, C. (2017). "Process-based interpretation of conceptual hydrological model performance using a multinational catchment set." Water Resources Research, Vol. 53, pp. 7247-7268. https://doi.org/10.1002/2016WR019991
  35. Priestley, C.H.B., and Taylor, R.J. (1972). "On the assessment of surface heat flux and evaporation using large-scale parameters." Monthly Weather Review, Vol. 100, pp. 81-92. https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2
  36. Pushpalatha, R., Perrin, C., Le Moine, N., Mathevet, T., and Andreassian, V. (2011). "A downward structural sensitivity analysis of hydrological models to improve low-flow simulation." Journal of Hydrology, Vol. 411, pp. 66-76. https://doi.org/10.1016/j.jhydrol.2011.09.034
  37. Quinn, J.D., Hadjimichael, A., Reed, P.M., and Steinschneider, S., (2020). "Can exploratory modeling of water scarcity vulnerabilities and robustness be scenario neutral?" Earth's Future, Vol. 8, e2020EF001650.
  38. Raje, D., and Mujumdar, P.P. (2011). "A comparison of three methods for downscaling daily precipitation in the Punjab region." Hydrological Processes, Vol. 25, pp. 3575-3589. https://doi.org/10.1002/hyp.8083
  39. Steinschneider, S., and Brown, C. (2013). "A semiparametric multivariate, multisite weather generator with low-frequency variability for use in climate risk assessments." Water Resources Research, Vol. 49, pp. 7205-7220. https://doi.org/10.1002/wrcr.20528
  40. Steinschneider, S., Wi, S., and Brown, C. (2015). "The integrated effects of climate and hydrologic uncertainty on future flood risk assessments." Hydrological Processes, Vol. 29, pp. 2823-2839. https://doi.org/10.1002/hyp.10409
  41. Szilagyi, J., Crago, R., and Qualls, R. (2017). "A calibration-free formulation of the complementary relationship of evaporation for continental-scale hydrology. Journal of Geophysical Research: Atmospheres, Vol. 122, pp. 264-278. https://doi.org/10.1002/2016JD025611
  42. Thinda, K.T., Ogundeji, A.A., Belle, J.A., Ojo, T.O. (2020). "Understanding the adoption of climate change adaptation strategies among smallholder farmers: Evidence from land reform beneficiaries in South Africa." Land Use Policy, Vol. 99, 104858. https://doi.org/10.1016/j.landusepol.2020.104858
  43. Trenberth, K.E., Dai, A., Van Der Schrier, G., Jones, P.D., Barichivich, J., Briffa, K.R., and Sheffield, J. (2014). "Global warming and changes in drought." Nature Climate Change, Vol. 4, pp. 17-22. https://doi.org/10.1038/nclimate2067
  44. Turner, T.E., Swindles, G.T., and Roucoux, K.H. (2014). "Late Holocene ecohydrological and carbon dynamics of a UK raised bog: impact of human activity and climate change." Quaternary Science Reviews, Vol. 84, pp. 65-85. https://doi.org/10.1016/j.quascirev.2013.10.030
  45. Weaver, C.P., Lempert, R.J., Brown, C., Hall, J.A., Revell, D., and Sarewitz, D. (2013). "Improving the contribution of climate model information to decision making: The value and demands of robust decision frameworks." WIREs Climate Change, Vol. 4, pp. 39-60. https://doi.org/10.1002/wcc.202
  46. Whateley, S., Steinschneider, S., and Brown, C., (2014). "A climate change range-based method for estimating robustness for water resources supply." Water Resources Research, Vol. 50, pp. 8944-8961. https://doi.org/10.1002/2014WR015956
  47. Yang, T., Gao, X., and Sorooshian, S., Li, X. (2016). "Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled crossvalidation scheme." Water Resources Research, Vol. 52, pp. 1626-1651. https://doi.org/10.1002/2015WR017394