A Study on the Development of a Dam Operation Table Using the Rainfall Matrix

강우 매트릭스를 활용한 댐 운영 조견표 개발에 관한 연구

  • 정창삼 (인덕대학교 토목환경공학과)
  • Received : 2020.03.25
  • Accepted : 2020.06.23
  • Published : 2020.06.30


Recently, flood damage has been increasing in Korea due to frequent local torrential rains caused by abnormal weather conditions. According to the calculation of the recurrence period of torrential rain that occurred in North Chungcheong Province on July 16, 2017, it was estimated that the rainfall frequency in the upper are of Goessan Dam was around 1,524 years, and the highest level of Goesan Dam rose to EL.137.60 meters, leaving only 5 cm of margin until the height of the dam floor (EL.137.65 meters). The Goesan Dam, which operated for 62 years since 1957, needs to be prepared to cope with the increase of floodgate volume in the basin, the development of a single purpose dam for power generation only, and there are no measurement facilities for flood control, so efficient operation methods are needed to secure the safety of residents in upper and lower regions. In this study, a method of dam operation was proposed by constructing a rain matrix for quick decision making in flood prediction, calculating the highest level of dam for each condition in advance, and preparing a survey table, and quickly finding the level corresponding to the conditions in case of a situation.


  1. Ahn, J. and Jeong, C. (2018). Numerical Simulation of the Flood Event Induced Temporally and Spatially Concentrated Rainfall - On August 17, 2017, the Flood Event of Cheonggyecheon. Korean Society of Disaster & Security. 11(2): 45-52.
  2. Chungcheongbuk-do (2017). Dalcheon River Basic Plan (Change) Report. Chungcheongbuk-do: Chungcheongbuk-do.
  3. Dam Hydrological Information. Water Resources Management Information System, (accessed 2020.3.20.).
  4. IPCC (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland. 151 p.
  5. KHNP (2012). Hydroelectric Power Plant Dam Management Guidelines. Gyeongsangbuk-do : Korea Hydro & Nuclear Power Co., Ltd.
  6. KHNP (2019). Final Report on the Research Service for the Efficient Operation of Goesan Dam. Gyeongsangbuk-do : Korea Hydro & Nuclear Power Co., Ltd.
  7. MOLIT (2017). 2011, Flood Damage Survey. Sejong City: Ministry of Land, Infrastructure and Transport.
  8. MOLIT (2018). Flood Prevention Program. Sejong City: Ministry of Land, Infrastructure and Transport.
  9. Yoo, H., Lee, S. O., Choi, S., and Park, M. (2019). A Study on the Data Driven Neural Network Model for the Prediction of Time Series Data: Application of Water Surface Elevation Forecasting in Hangang River Bridge. Korean Society of Disaster & Security. 12(2): 73-82.