DOI QR코드

DOI QR Code

Flood inflow forecasting on HantanRiver reservoir by using forecasted rainfall

LDAPS 예측 강우를 활용한 한탄강홍수조절댐 홍수 유입량 예측

  • Received : 2016.02.03
  • Accepted : 2016.02.29
  • Published : 2016.04.30

Abstract

Due to climate changes accelerated by global warming, South Korea has experienced regional climate variations as well as increasing severities and frequencies of extreme weather. The precipitation in South Korea during the summer season in 2013 was concentrated mainly in the central region; the maximum number of rainy days were recorded in the central region while the southern region had the minimum number of rainy days. As a result, much attention has been paid to the importance of flood control due to damage caused by spatiotemporal intensive rainfalls. In this study, forecast rainfall data was used for rapid responses to prevent disasters during flood seasons. For this purpose, the applicability of numerical weather forecast data was analyzed using the ground observation rainfall and inflow rate. Correlation coefficient, maximum rainfall intensity percent error and total rainfall percent error were used for the quantitative comparison of ground observation rainfall data. In addition, correlation coefficient, Nash-Sutcliffe efficiency coefficient, and standardized RMSE were used for the quantitative comparison of inflow rate. As a result of the simulation, the correlation coefficient up to six hours was 0.7 or higher, indicating a high correlation. Furthermore, the Nash-Sutcliffe efficiency coefficient was positive until six hours, confirming the applicability of forecast rainfall.

최근 우리나라는 지구 온난화에 의한 기후 변화로 지역별 기후 변동뿐만 아니라 극한 기상 발생의 규모와 빈도가 커지고 있다. 2013년 장마 전선이 주로 중부지방에 위치하여 중부지방에서 강수일수 최고값을 기록하였으며, 남부지방은 강수일수 최저값을 기록하였다. 이러한 강우의 공간적, 시간적 집중 현상으로 호우 피해가 발생하여 치수의 중요성이 부각되고 있다. 본 연구에서는 홍수기 신속한 홍수 방재를 위하여 예측 강우 자료를 활용하고자 한다. 이를 위해 수치 예보 자료의 적용 가능성을 지상 관측 강우 및 유입량을 이용하여 분석하였다. 지상 관측 강우와 정량적 비교를 위해 상관계수, 최대 강우강도 퍼센트 오차 및 총 강우량 퍼센트 오차 등을 이용하였으며, 유입량은 상관계수, Nash-Sutcliffe 효율계수, 표준화된 RMSE를 사용하였다. 모의 결과 6시간까지의 상관계수는 0.7 이상으로 높은 상관성을 나타내었으며, Nash-Sutcliffe 효율계수는 6시간까지 양수를 나타내어 예측 강우의 활용 가능성을 확인할 수 있었다.

Keywords

References

  1. Byeon, D.H. (2009). Dam Inflow Forecasts Using Short-Term Numerical Weather Forecast Data, Sejong University.
  2. Charney, J.G. (1948). "On the scale of atmospheric motions", Geofysiske Publikasjoner, Vol. 17, No. 2, 1948.
  3. Cho, M.R. (1991). Political Economy of Regional Differentiation, Seoul, Hanul.
  4. Cho, M.R. (2003). "Trend and Prospect of Urbanization in Korea: Reflections on Korean Cities", Economy and Society, Vol. 60, pp. 10-39.
  5. Cho, S.H. (2011). Development of a Precipitation Forecast Model Using Satellite Data and Ground Network Data, Kyungpook National University.
  6. GAR, Global Assessment Report on disaster risk reduction (2015). Internationally Reported Losses 1990-2014 EMDAT, Retrieved from http://www.preventionweb.net/english/hyogo/gar/2015/en/home/data.php.
  7. Han, M.S. (2014). Correction from MAPLE and KLAPS rainfall forecasting.
  8. Kim, B.K., Jang, D.W., Yang, D.M., and Yoo, C.S. (2009). "Accuracy Consideration of MAPLE Data Vary Short-Term Forecasting Model.", Water for Future, Korea Water Resources Association, Korea, Vol. 42, No. 12, pp. 52-64.
  9. Kim, J.H., Bae, D.H., and Kim, W.T. (2005). "Hydrological Rainfall Forecast for Short-Range Numerical Weather Prediction Model", Korean Society of Civil Engineers, KSCE Convention, pp. 1493-1496.
  10. Lee, S.H., and Heo, I.H. (2011). "The Impacts of Urbanization on Changes of Extreme Events of Air Trmperature in South Korea", Journal of the Korean Geographical Society, Vol. 46, No. 3, pp. 257-276.
  11. Lee, S.J., Jeong, C.S., Kim, J.C., and Hwang, M.H. (2011). "Long-term Streamflow Prediction Using ESP and RDAPS Model", Journal of Korea Water Resources Association, Korea Water Resources Association, Vol. 44, No. 12, pp. 967-974. https://doi.org/10.3741/JKWRA.2011.44.12.967
  12. Legates, D.R., and McCabe, G.J. (1999). "Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation", Water Resources Research, Vol. 35, No. 1, pp. 233-241. https://doi.org/10.1029/1998WR900018
  13. Ministry of Public Safety and Security (2014). Chronology of Disasters.
  14. Richardson, L.F. (1922). Weather Prediction by Numerical Process. Cambridge University Press. 2nd Edn. with Foreward by Peter Lynch (2007).
  15. So, C.H. (2015). "Climate Change Adaptation and Business", Risk Management, Vol. 136, pp. 50-54.
  16. Wilcox, B.P., Rawls, W.J., Brakensiek, D.L., and Wight, J.R. (1990). "Predicting runoff from rangeland catchments : A comparison of two models", Water Resources Research, Vol. 26, Issue 10, pp. 2401-2410. https://doi.org/10.1029/WR026i010p02401
  17. WMO (1994). Guide to Hydrological Practices-Fifth Edition, WMO-No. 168.

Cited by

  1. Analysis on the sediment sluicing efficiency by variation of operation water surface elevation at flood season vol.49, pp.12, 2016, https://doi.org/10.3741/JKWRA.2016.49.12.971
  2. Development of Initial Design-Width Formulas for Small Streams: Case Study in Western Gangwon Province vol.18, pp.6, 2018, https://doi.org/10.9798/KOSHAM.2018.18.6.357