Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Assessment of water supply reliability in the Geum River Basin using univariate climate response functions: a case study for changing instreamflow managements

단변량 기후반응함수를 이용한 금강수계 이수안전도 평가: 하천유지유량 관리 변화를 고려한 사례연구

  • Kim, Daeha (Department of Civil Engineering, Jeonbuk National University) ;
  • Choi, Si Jung (Department of Hydro Science and Engineering Research, Korea Institute of Civil and Building Technology) ;
  • Jang, Su Hyung (Water Resources and Environmental Research Center, K-water Research Institute) ;
  • Kang, Dae Hu (CEO, Dae-Sung Engineering Co., Ltd.)
  • 김대하 (전북대학교 토목환경자원에너지공학부) ;
  • 최시중 (한국건설기술연구원 수자원하천연구본부) ;
  • 장수형 (K-water 연구원 수자원환경연구소) ;
  • 강대후 ((주)대성기술)
  • Received : 2023.11.02
  • Accepted : 2023.12.12
  • Published : 2023.12.31
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Abstract

Due to the increasing greenhouse gas emissions, the global mean temperature has risen by 1.1℃ compared to pre-industrial levels, and significant changes are expected in functioning of water supply systems. In this study, we assessed impacts of climate change and instreamflow management on water supply reliability in the Geum River basin, Korea. We proposed univariate climate response functions, where mean precipitation and potential evaporation were coupled as an explanatory variable, to assess impacts of climate stress on multiple water supply reliabilities. To this end, natural streamflows were generated in the 19 sub-basins with the conceptual GR6J model. Then, the simulated streamflows were input into the Water Evaluation And Planning (WEAP) model. The dynamic optimization by WEAP allowed us to assess water supply reliability against the 2020 water demand projections. Results showed that when minimizing the water shortage of the entire river basin under the 1991-2020 climate, water supply reliability was lowest in the Bocheongcheon among the sub-basins. In a scenario where the priority of instreamflow maintenance is adjusted to be the same as municipal and industrial water use, water supply reliability in the Bocheongcheon, Chogang, and Nonsancheon sub-basins significantly decreased. The stress tests with 325 sets of climate perturbations showed that water supply reliability in the three sub-basins considerably decreased under all the climate stresses, while the sub-basins connected to large infrastructures did not change significantly. When using the 2021-2050 climate projections with the stress test results, water supply reliability in the Geum River basin was expected to generally improve, but if the priority of instreamflow maintenance is increased, water shortage is expected to worsen in geographically isolated sub-basins. Here, we suggest that the climate response function can be established by a single explanatory variable to assess climate change impacts of many sub-basin's performance simultaneously.

대기온실가스 증가로 전지구 평균기온은 산업화 이전 대비 1.1℃ 상승했고 수자원시스템의 공급능력에 상당한 변화가 예상된다. 본 연구에서는 금강수계 내 여러 중권역의 이수안전도와 기후조건의 관계(기후반응함수)를 단변량 함수로 나타내 기후민감도를 동시에 평가할 수 있는 방법을 제안하였다. 사례연구를 위해 GR6J 모형으로 중권역별 자연유출을 모의했고 이를 Water Evaluation And Planning (WEAP) 최적모형에 입력해 2030년 수요전망에 대한 공급신뢰도를 평가하였다. 여러 중권역의 이수안전도를 동시에 비교하기 위해 평균 강수량과 잠재증발산량의 비율을 독립변수 사용하여 단변량 기후민감도 함수를 개발하였다. 사례연구 결과, 1991-2020 자연유출을 이용해 수계전체 물부족을 최소화시키는 운영을 가정했을 때 공급신뢰도는 19개 중권역 중 보청천유역에서 가장 낮았다. 하천유지유량의 우선순위를 농업용수와 생공용수과 동일하게 조정한 시나리오에서는 보청천유역, 초강유역, 논산천유역의 이수안전도가 크게 감소하는 것으로 나타났다. 보청천유역, 초강유역, 논산천유역의 이수안전도는 모든 기후스트레스 테스테에서 크게 감소한 반면, 미호강유역, 금강공주유역, 금강하구유역은 아주 건조한 기후조건에서만 이수안전도가 감소했다. 대규모 인프라에서의 공급이 원활한 중권역의 기후민감도는 크게 변하지 않았다. 2021-2050 기후전망을 민감도함수에 적용했을 때 금강수계의 공급신뢰도는 대체로 좋아질 가능성이 높지만 하천유지유량 우선순위를 높이게 되면 지형적, 인위적으로 고립된 중권역에서 물부족은 심해질 것으로 분석되었다. 2021-2050기간 금강수계의 이수안전도는 기후스트레스 보다 하천관리정책의 변화에 더 큰 영향을 받을 것으로 판단된다.

Keywords

References

  1. Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. The Food and Agriculture Organization of the United Nations, Rome.
  2. Apipattanavis, S., Podesta, G., Rajagopalan, B., and Katz, R.W. (2007). "A semiparametric multivariate and multisite weather generator." Water Resources Research, Vol. 43, W11401.
  3. Berghuijs, W.R., Larsen, J.R., van Emmerik, T.H.M., and Woods, R.A. (2017). "A global assessment of runoff sensitivity to changes in precipitation, potential evaporation, and other factors." Water Resources Research, Vol. 53, pp.8475-8486. https://doi.org/10.1002/2017WR021593
  4. 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
  5. 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
  6. 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.
  7. Brown, C., Steinschneider, S., Ray, P., Wi, S., Basdekas, L., and Yates, D. (2019). "Decision Scaling (DS): Decision support for climate change." Decision Making under Deep Uncertainty: From Theory To Practice, Edited by Marchau, V.A.W., Walker, W.E., Bloemen, P.J.T.M., and Popper, S.W., Springer, Cham, Switzerland, pp. 255-287.
  8. 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: A Journal of the Royal Meteorological Society, Vol. 28, No. 15, pp. 2031-2064. https://doi.org/10.1002/joc.1688
  9. Dilling, L., Daly, M.E., Travis, W.R., Wilhelmi, O.V., and Klein, R.A. (2015). "The dynamics of vulnerability: Why adapting to climate variability will not always prepare us for climate change." Wiley Interdisciplinary Reviews: Climate Change, Vol. 6, No. 4, pp. 413-425. https://doi.org/10.1002/wcc.341
  10. Feng, H., and Zhang, M. (2015). "Global land moisture trends: Drier in dry and wetter in wet over land." Scientific Reports, Vol. 5, 18018.
  11. 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
  12. 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
  13. 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
  14. Intergovernmental Panel on Climate Change (IPCC) (2021). 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., Cambridge University Press, Cambridge, United Kingdom and New York, NY, U.S., pp. 1513-1765.
  15. Intergovernmental Panel on Climate Change (IPCC) (2023). Summary for policymakers. Climate change 2023: Synthesis report. Contribution of working groups I, II and III to the sixth assessment report of the intergovernmental panel on climate change, Edited by Core Writing Team, Lee, H., and Romero J., Geneva, Switzerland, pp. 1-34.
  16. Jander, V., Vicuna, S., Melo, O., and Lorca, A. (2023). "Adaptation to climate change in basins within the context of the water-energy-food nexus." Journal of Water Resources Planning and Management, Vol. 149, 04023060. https://doi.org/10.1061/JWRMD5.WRENG-5566
  17. Jang, O.J., Moon, Y.I., and Moon, H.T. (2021). "Methodology for assessment and forecast of drought severity based on the water balance analysis." Journal of Korea Water Resources Association, Vol. 54, No. 4, pp.241-254. https://doi.org/10.3741/JKWRA.2021.54.4.241
  18. 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 Change Research, Vol. 6, pp.331-334. https://doi.org/10.15531/ksccr.2015.6.4.331
  19. 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.
  20. 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
  21. Kim, D., Chun, J.A., and Choi, S.J. (2019a). "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
  22. Kim, D., Eum, H.I., Kaluarachchi, J.J., and Chun, J.A. (2019b). "A sensitivity-based analysis for managing storage capacity of a small agricultural reservoir under drying climate." Agricultural Water Management, Vol. 213, pp. 410-418. https://doi.org/10.1016/j.agwat.2018.10.040
  23. Kim, D., Jung, I.W., and Chun, J.A. (2017). "A comparative assessment of rainfall-runoff modelling against regional flow duration curves for ungauged catchments." Hydrology and Earth System Sciences, Vol. 21, pp.5647-5661. https://doi.org/10.5194/hess-21-5647-2017
  24. 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.
  25. Lee, S.C., and Kim, D. (2023). "A comparative study of conceptual model and machine learning model for rainfall-runoff simulation." Journal of Korea Water Resources Association, Vol. 56, pp. 563-574. https://doi.org/10.3741/JKWRA.2023.56.9.563
  26. Ma, N., Szilagyi, J., and Zhang, Y. (2021). "Calibration-free complementary relationship estimates terrestrial evapotranspiration globally." Water Resources Research, Vol. 57, e2021WR029691.
  27. Mianabadi, A., Davary, K., Pourreza-Bilondi, M., and CoendersGerrits, A.M.J. (2020). "Budyko framework; Towards nonsteady state conditions." Journal of Hydrology, Vol. 588, 125089.
  28. Ministry of Environment (ME) (2021). The 1st national water management plan (2021-2030).
  29. Ministry of Land, Infrastructure and Transport (MOLIT) (2016). National water resources plan (2001-2020) - 3rd revision (2016-2020).
  30. No, S.-H., Jung, K.S., Park, J.H., and Ryoo, K.S. (2013). "Water supply change outlook for Geum River Basin considering RCP climate change scenario." Journal of Korea Water Resources Association, Vol. 46, pp. 505-517. https://doi.org/10.3741/JKWRA.2013.46.5.505
  31. O'Neill, B.C., Tebaldi, C., Van Vuuren, D.P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.F., Lowe, J., and Meehl, G.A. (2016). "The scenario model intercomparison project (ScenarioMIP) for CMIP6." Geoscientific Model Development, Vol. 9, pp.3461-3482. https://doi.org/10.5194/gmd-9-3461-2016
  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. 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
  35. Prudhomme, C., Wilby, R.L., Crooks, S., Kay, A.L., and Reynard, N.S. (2010). "Scenario-neutral approach to climate change impact studies: Application to flood risk." Journal of Hydrology, Vol. 390, pp. 198-209. https://doi.org/10.1016/j.jhydrol.2010.06.043
  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. Roderick, M.L., and Farquhar, G.D. (2011). "A simple framework for relating variations in runoff to variations in climatic conditions and catchment properties." Water Resources Research, Vol. 47, No. 12, W00G07.
  38. Schlef, K.E., Steinschneider, S., and Brown, C.M. (2017). "Spatiotemporal impacts of climate and demand on water supply in the Apalachicola-Chattahoochee-Flint Basin." Journal of Water Resources Planning and Management, Vol. 144, 05017020.
  39. Schlef, K.E., Steinschneider, S., and Brown, C.M. (2018). "Spatiotemporal impacts of climate and demand on water supply in the Apalachicola-Chattahoochee-Flint Basin." Journal of Water Resources Planning and Management, Vol. 144, 05017020.
  40. Seneviratne, S.I., Corti, T., Davin, E.L., Hirschi, M., Jaeger, E.B., Lehner, I., Orlowsky, B., and Teuling, A.J. (2010). "Investigating soil moisture-climate interactions in a changing climate: A review." Earth-Science Reviews, Vol. 99, No. 3-4, pp. 125-161. https://doi.org/10.1016/j.earscirev.2010.02.004
  41. 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
  42. Szilagyi, J. (2021). "On the thermodynamic foundations of the complementary relationship of evaporation." Journal of Hydrology, Vol. 593, 125916.
  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, S.W., Marlow, D., Ekstrom, M., Rhodes, B.G., Kularathna, U., and Jeffrey, P.J. (2014). "Linking climate projections to performance: A yield-based decision scaling assessment of a large urban water resources system." Water Resources Research, Vol. 50, No. 4, pp. 3553-3567. https://doi.org/10.1002/2013WR015156
  45. Wang, B., Liang, X.J., Zhang, H., Wang, L., and Wei, Y.M. (2014). "Vulnerability of hydropower generation to climate change in China: Results based on Grey forecasting model." Energy Policy, Vol. 65, pp.701-707. https://doi.org/10.1016/j.enpol.2013.10.002
  46. 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
  47. 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
  48. Xiong, J., Guo, S., Abhishek, Chen, J., and Yin, J. (2022). "Global evaluation of the "dry gets drier, and wet gets wetter" paradigm from a terrestrial water storage change perspective." Hydrology and Earth System Sciences, Vol. 26, pp. 6457-6476. https://doi.org/10.5194/hess-26-6457-2022
  49. Yang, T., Ding, J., Liu, D., Wang, X., and Wang, T. (2019). "Combined use of multiple drought indices for global assessment of dry gets drier and wet gets wetter paradigm." Journal of Climate, 32, pp. 737-748. https://doi.org/10.1175/JCLI-D-18-0261.1
  50. Zhou, S., Williams, A.P., Berg, A.M., Cook, B.I., Zhang, Y., Hagemann, S., Lorenz, R., Seneviratne, S.I., and Gentine, P. (2019). "Land-atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity." Proceedings of the National Academy of Sciences, Vol. 116, pp. 18848-18853.  https://doi.org/10.1073/pnas.1904955116