Long-term Simulation and Uncertainty Quantification of Water Temperature in Soyanggang Reservoir due to Climate Change

기후변화에 따른 소양호의 수온 장기 모의 및 불확실성 정량화

  • Yun, Yeojeong (Department of Environmental Engineering, Chungbuk National University) ;
  • Park, Hyungseok (Department of Environmental Engineering, Chungbuk National University) ;
  • Chung, Sewoong (Department of Environmental Engineering, Chungbuk National University) ;
  • Kim, Yongda (Department of Statistics, Seoul National University) ;
  • Ohn, Ilsang (Department of Statistics, Seoul National University) ;
  • Lee, Seoro (Department of Regional Infrastructure Engineering, Kangwon National University)
  • Received : 2019.09.30
  • Accepted : 2020.01.23
  • Published : 2020.01.30


Future climate change may affect the hydro-thermal and biogeochemical characteristics of dam reservoirs, the most important water resources in Korea. Thus, scientific projection of the impact of climate change on the reservoir environment, factoring uncertainties, is crucial for sustainable water use. The purpose of this study was to predict the future water temperature and stratification structure of the Soyanggang Reservoir in response to a total of 42 scenarios, combining two climate scenarios, seven GCM models, one surface runoff model, and three wind scenarios of hydrodynamic model, and to quantify the uncertainty of each modeling step and scenario. Although there are differences depending on the scenarios, the annual reservoir water temperature tended to rise steadily. In the RCP 4.5 and 8.5 scenarios, the upper water temperature is expected to rise by 0.029 ℃ (±0.012)/year and 0.048 ℃ (±0.014)/year, respectively. These rise rates are correspond to 88.1 % and 85.7 % of the air temperature rise rate. Meanwhile, the lower water temperature is expected to rise by 0.016 ℃ (±0.009)/year and 0.027 ℃ (±0.010)/year, respectively, which is approximately 48.6 % and 46.3 % of the air temperature rise rate. Additionally, as the water temperatures rises, the stratification strength of the reservoir is expected to be stronger, and the number of days when the temperature difference between the upper and lower layers exceeds 5 ℃ increases in the future. As a result of uncertainty quantification, the uncertainty of the GCM models showed the highest contribution with 55.8 %, followed by 30.8 % RCP scenario, and 12.8 % W2 model.


Supported by : 한국환경산업기술원


  1. Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., Santhi, C., Harmel, R. D., Griensven, A. V., Liew, M. W. V., Kannan, N., and Jha, M. K. (2012). SWAT: Model use, calibration, and validation, Transactions of the ASABE, 55(4), 1491-1508.
  2. Bae, D. H., Jung, I. W., and Kwon, W. T. (2007). Generation of high scenarios for climate impacts on water resources (I): climate scenarios on each sub-basins, Journal of Korea Water Resources Association, 40(3), 191-204. [Korean Literature]
  3. Bastola, S., Murphy, C., and Sweeney, J. (2011). The role of hydrological modelling uncertainties in climate change impact assessments of irish river catchments, Advances in Water Resources, 34(5), 562-576.
  4. Borsuk, M., Clemen, R., Maguire, L., and Reckhow, K. (2001). Stakeholder values and scientific modeling in the Neuse River watershed, Group Decision and Negotiation, 10(4), 355-373.
  5. Bowen, J. D. and Hieronymus, J. W. (2003). A CE-QUAL-W2 model of Neuse Estuary for total maximum daily load development, Journal of Water Resources Planning and Management, 129(4), 283-294.
  6. Bronstert, A. (2003). Floods and climate change: interactions and impacts, Risk Analysis: An International Journal, 23(3), 545-557.
  7. Chen, J., Brissette, F. P., Poulin, A., and Leconte, R. (2011). Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed, Water Resources Research, 47, W12509.
  8. Chung, S. W., Park, H. S., Yoon, S. W., and Ryu, I. G. (2011). Effect of installing a selective withdrawal structure for the control of turbid water in Soyang reservoir, Journal of Korean Society on Water Environment, 27(6), 743-753. [Korean Literature]
  9. Choi, J., Ahn, J., Kim, K. S., and Lim, K. J. (2007). Evaluation of SWAT applicability to simulation of sediment behaviors at the Imha-Dam Watershed, Journal of Korean Society on Water Environment, 23(4), 467-473. [Korean Literature]
  10. Cole, T. M. and Wells, S. A. (2017). CE-QUAL-W2: A two-dimensional, laterally averaged, hydrodynamic and water quality model, version 4.1, Department of Civil and Environmental Engineering Portland State University.
  11. Debele, B., Srinivasan, R., and Parlange, J. Y. (2008). Coupling upland watershed and downstream waterbody hydrodynamic and water quality models (SWAT and CE-QUAL-W2) for better water resources management in complex river basins, Environmental Modeling & Assessment, 13(1), 135-153.
  12. Dessai, S. and Hulme, M. (2011). Assessing the robustness of adaptation decisions to climate change uncertainties : A case study on water resources management in the East of England, Global Environmental Change, 17, 59-72.
  13. Fang, X. and Stefan, H. G. (2009). Simulations of climate effects on water temperature, dissolved oxygen, and ice and snow covers in lakes of the contiguous US under past and future climate scenarios, Limnology and Oceanography, 54(6part2), 2359-2370.
  14. Foley, B., Jones, I. D., Maberly, S. C., and Rippey, B. (2012). Long-term changes in oxygen depletion in a small temperate lake: Effects of climate change and eutrophication, Freshwater Biology, 57(2), 278-289.
  15. Gillingham, K., Nordhaus, W. D., Anthoff, D., Blanford, G., Bosetti, V., Christensen, P., McJeon, H., Reilly, J., and Sztorc, P. (2015). Modeling uncertainty in climate change: A multi-model comparison, No. w21637, National Bureau of Economic Research,Working Paper 21637, NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138.
  16. Han, J. H., Lee, D. J., Kang, B. S., Chung, S. W., Jang, W. S., Lim, K. J., and Kim, J. G. (2017). Potential impacts of future extreme storm events on streamflow and sediment in Soyang-dam watershed, Journal of Korean Society on Water Environment, 33(2), 160-169. [Korean Literature]
  17. Hwang, I. C. (2017). A review on probabilistic climate-economy models and an application of FUND, Environmental and Resource Economics Review, 26(3), 359-398.
  18. Intergovernmental Panel on Climate Change (IPCC). (2007). Climate change 2007, The physical science basis. Agenda, 6(07), 333.
  19. Intergovernmental Panel on Climate Change (IPCC). (2014). Mitigation of climate change, Contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change, 1454.
  20. International Steategy for Disaster Reduction (ISDR). (2007). Risk Reduction: 2007 Global Review, International Steategy for Disaster Reduction.
  21. Jackson, C. R., Meister, R., and Prudhomme, C. (2011). Modelling the effects of climate change and its uncertainty on uk chalk groundwater resources from an ensemble of global climate model projections, Journal of Hydrology, 399, 12-28.
  22. Jeong, C. S., Heo, J. H., and Bae, D. H. (2004). Uncertainty analysis of GCM information in Korea using probabilistic diagnostics. Journal of Korea Water Resources Association, 37(3), 173-184.
  23. Jung, I. W., Bae, D. H., and Kim, G. (2011). Recent trends of mean and extreme precipitation in Korea, International journal of climatology, 31(3), 359-370. [Korean Literature]
  24. Joung, S. H., Park, H. K., Lee, H. J., and Lee, S. H., (2013). Effect of climate change for diatom bloom at winter and spring season in Mulgeum station of the Nakdong river, South Korea, Journal of Korean Society on Water Environment, 29(2), 155-164. [Korean Literature]
  25. Kay, A. L., Davies, H. N. ,Bell, V. A., and Jones, R. G. (2009). Comparison of uncertainty sources for climate change impacts : flood frequency in England, Climatic Changes, 92, 41-63.
  26. Kim, B. C. and Jung, S. M. (2007). Turbid storm runoffs in lake Soyang and their environmental effect, Journal of the Korean Society of environmental engineers, 29(11), 1185-1190. [Korean Literature]
  27. Kim, B. C. and Kim, Y. H. (2004). Articles: phosphorus cycle in a deep reservoir in Asian monsoon area(lake Soyang, Korea) and the modeling with a 2-D hydrodynamic water quality model [CE-QUAL-W2], Korean Journal of Limnology, 37(2), 205-212. [Korean Literature]
  28. Kim, B. S., Kwon, H. H., and Kim, H. S. (2011). Impact assessment of climate change on drought risk, Journal of Wetlands Researh, 13(1), 1-11. [Korean Literature]
  29. Kim, Y., Ohn, I., Lee, J. K., and Kim, Y. O. (2019). Generalizing uncertainty decomposition theory in climate change impact assessments, Journal of Hydrology X, 3, 100024.
  30. Kim, Y. H., Kim, B. C., Choi, K. S., and Seo, D. I. (2001). Modeling of thermal stratification and transport of density flow in Soyang reservoir using the 2-D hydrodynamic water quality model, CE-QUAL-W2, Journal of the Korean Society of Water and Wastewater, 18, 96-105. [Korean Literature]
  31. Kundzewicz, Z. W., Kanae, S., Seneviratne, S. I., Handmer, J., Nicholls, N., Peduzzi, P., Mechler, R., Bouwer, L. M., Arnell, N., Mach, K., Muir-Wood, R. Brakenridge, G. R., Kron, W., Benito, G., Honda, Y., Takahashi, K., and Sherstyukov, B. (2014). Flood risk and climate change: global and regional perspectives, Hydrological Sciences Journal, 59(1), 1-28.
  32. K-water. (1994). Research Report on the Reservoir of Soyang River Dam, K-water. [Korean Literature]
  33. K-water. (2005). Multipurpose Dam Operation Practice Manual, K-water. [Korean Literature]
  34. K-water. (2007). Multipurpose dam (four dams including Soyanggang Dam) Establishment of turbid water reduction plan (Soyanggang Dam), K-water. [Korean Literature]
  35. Lee, J. K. and Kim, Y. O. (2015). Verification of bias corrected simulations of climate models using entropy, Journal of the Korean Society of Hazard Mitigation, 15(5), 25-35.
  36. Lee, J. W., Eom, J. S., Kim, B. C., Jang, W. S., Ryu, J. C., Kang, H. W., Kim, K. S., and Lim, K. J. (2011). Water quality prediction at Mandae watershed using SWAT and water quality improvement with vegetated filter strip, Journal of the Korean Society of Agricultural Engineers, 53(1), 37-45. [Korean Literature]
  37. Lee, K. H. (2016). Prediction of climate-induced water temperature using nonlinear air-water temperature relationship for aquatic environments, Journal of Environmental Science International, 25(6), 877-888. [Korean Literature]
  38. Minville, M., Brissette, F., and Leconte, R. (2008). Uncertainty of the impact of climate change on the hydrology of a Nordic watershed, Journal of hydrology, 358, 70-83.
  39. Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R. (2009). Soil and water assessment tool theoretical documentation version 2009, Texas Water Resources Institute.
  40. Noh, S., Park, H., Choi, H., and Lee, J. (2014). Effect of climate change for cyanobacteria growth pattern in Chudong station of lake Daechung, Journal of Korean Society on Water Environment, 30(4), 377-385. [Korean Literature]
  41. Paerl, H. W. and Huisman, J. (2009). Climate change: a catalyst for global expansion of harmful cyanobacterial blooms, Environmental microbiology reports, 1(1), 27-37.
  42. Paerl, H. W. and Paul, V. J. (2012). Climate change: links to global expansion of harmful cyanobacteria, Water research, 46(5), 1349-1363.
  43. Paltsev, S. (2017). Energy scenarios: The value and limits of scenario analysis, Wiley Interdisciplinary Reviews: Energy and Environment, 6(4), e242.
  44. Parson, E., Burkett, V., Fisher-Vanden, K., Keith, D., Mearns, L., Pitcher, H., Rosenzweig, C., and Webster, M. (2015). Global change scenarios: Their development and use. (Elizabeth L. Malone), Synthesis and Assessment Products, 2.1b. Washington, D.C.: U.S. Climate Change Science Program.
  45. Piccolroaz, S., Toffolon, M., and Majone, B. (2015). The role of stratification on lakes' thermal response: The case of L ake S uperior. Water Resources Research, 51(10), 7878-7894.
  46. Prudhomme, C. and Davies, H. (2009). Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part1: baseline climate, Climatic Change, 93, 177-195.
  47. Sheffield, J. and Wood, E. F. (2008). Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations, Climate dynamics, 31(1), 79-105.
  48. Shin, Y. and Jung, H. (2015). Assessing uncertainty in future climate change in Northeast Asia using multiple CMIP5 GCMs with four RCP scenarios, Journal of Environmental Impact Assessment, 24(3), 205-216.
  49. Stefan, H. G., Hondzo, M., Fang, X., Eaton, J. G., and McCormick, J. H. (1996). Simulated long-term temperature and dissolved oxygen characteristics of lakes in the north-central United States and associated fish habitat limits, Limnology and Oceanography, 41(5), 1124-1135.
  50. Trenberth, K. E., Dai, A., Van Der Schrier, G., Jones, P. D., Barichivich, J., Briffa, K. R., and Sheffield, J. (2013). Global warming and changes in drought, Nature Climate Change, 4(1), 17-22.
  51. Trutnevyte, E., Guivarch, C., Lempert, R., and Strachan, N. (2016). Reinvigorating the scenario technique to expand uncertainty consideration, Climatic change, 135(3-4), 373-379.
  52. Wagner, C. and Adrian, R. (2009). Cyanobacteria dominance: quantifying the effects of climate change, Limnology and Oceanography, 54(6part2), 2460-2468.
  53. Walker, W. E., Harremoes, P., Rotmans, J., Van Der Sluijs, J. P., Van Asselt, M. B., Janssen, P., and Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.
  54. Wilby, R. L. and Harris, I. (2006). A framework for assessing uncertainties in climate change impacts : Low-flow scenarios for the River Thames, U.K., Water Resources Research, 42(2), W02419.
  55. Winslow, L., Read, J., Woolway, R., Brentrup, J., Leach, T., Zwart, J., Albert, S. and Collinge, D. (2018). Zenodo, Available online: (accessed on 6 October 2018).
  56. Woolway, R. I., Maberly, S. C., Jones, I. D., and Feuchtmayr, H. (2014). A novel method for estimating the onset of thermal stratification in lakes from surface water measurements, Water Resources Research, 50(6), 5131-5140.
  57. Ye, L., Yoon, S. W., and Chung, S. W. (2008). Application of SWAT for the estimation of soil loss in the Daecheong dam basin, Journal of Korea Water Resources Association, 41(2), 149-162. [Korean Literature]
  58. Yi, H. S., Kim, D. S., Hwang, M. H., and An, K. G. (2016). Assessment of runoff and water temperature variations under RCP climate change scenario in Yongdam dam watershed, South Korea, Journal of Korean Society on Water Environment, 32(2), 173-182. [Korean Literature]
  59. Yu, J. J., Lee, H. J., Lee, K. L., Lyu, H. S., Whang, J. H., Shin, L. Y., and Chen, S. U. (2014). Relationship between distribution of the dominant phytoplankton species and water temperature in the Nakdong river, Korea, The Korean Society of Limnology, 47(4), 247-257. [Korean Literature]
  60. Yun, Y. J., Park, H. S., and Chung, S. W. (2019). Projection of water temperature and stratification strength with climate change in Soyanggang Reservoir in South Korea, Journal of Korean Society on Water Environment, 35(3), 234-247. [Korean Literature]