Journal of Korean Society of Disaster and Security (한국방재안전학회논문집)

Korean Society of Disaster & Security (한국방재안전학회)
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Analysis of Statistical Characteristics of Annual Precipitation in Korea Using Data Screeening Technique

데이터 스크린 기법을 이용한 연강수량의 통계적 특성 분석

  • Jeung, Se-Jin (Climate Disaster Big Data Center, Kangwon Institute of Inclusive Technoligy, Kangwon National University) ;
  • Lim, Ga-Kyun (Disaster Prevention Department, Kyongbo Engineering co.LTD) ;
  • Kim, Byung-Sik (Department of Urban & Environmental Disaster Prevention Engineering, Kangwon National University)
  • 정세진 (강원대학교 강원종합기술연구원) ;
  • 임가균 ((주)경보기술단 방재부) ;
  • 김병식 (강원대학교 도시환경방재공학전공)
  • Received : 2020.08.25
  • Accepted : 2020.09.20
  • Published : 2020.09.30
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Abstract

Hydrological data is very important in understanding the hydrological process and identifying its characteristics to protect human life and property from natural disasters. In particular, hydrological analysis are often performed assuming that hydrological data are stationary. However, recently climate change has raised the issue of climate stationary, and it is necessary to analyze the nonstationary of the climate. In this study, a method to analyze the stationarity of hydrological data was examined using the annual precipitation of 37 meteorological stations with long - term record data. Therefore, in this study, the stationary was determined by analyzing the persistence, trend, and stability using annual precipitation. Overall results showed that a trend was observed in 4 out of 37 stations, stable was investigated at 15 stations, and persistence was shown at 4 stations. In the stationary analysis using the annual precipitation data, 25 stations (67% of 37 stations) were nonstationary.

본 논문에서는 미계측 유역에 적용할 수 있는 갈수지수 산정 회귀모형을 개발하고자 하였다. 30개의 중권역 유역을 대상으로 국가수자원종합관리시스템에서 제공하는 장기유출자료를 이용하여 평균 갈수량과 평균저수량, 지속기간별 빈도별 갈수지수를 산정하였으며 이를 유역특성인자 18개와 기상특성인자 3개와의 상관 분석을 통하여 최종적으로 유역면적, 유역 평균 표고, 유역 평균 경사, 수계 밀도, 유출곡선지수, 연증발산량, 연강수량을 선정하여 다중회귀분석을 수행하여 갈수지수 회귀모형을 개발하였다. 개발된 회귀모형을 평가하기 위하여 10개의 검증유역을 미계측 유역으로 간주하여 평균제곱근오차(RMSE) 와 평균절대오차(MAE)를 이용하여 정확도를 추정하였다. 또한 기존의 평균갈수량 산정 회귀모형과의 비교를 통하여 본 논문에서 개발한 모형의 우수성을 검토하였다. 기존의 미계측 유역의 평균 갈수량 회귀모형과 비교·분석에서 보다 우수한 결과를 나타내었는데 이는 기존의 회귀모형보다 다양한 유역 특성인자와 수문특성인자를 고려하여 회귀모형을 개발하였기 때문인 것으로 판단된다.

Keywords

References

  1. Dahmen, E. R. and Hall, M. J. (1990). Screening of Hydrological Data: Tests for Stationarity and Relative Consistency. International Institute for Land Reclamation and Improvement.
  2. Jeung, S. J., Park, J. Y., Yang, D. M., and Kim, B. S. (2019). The Future of Extreme Climate Change in the Korean Peninsula Using National Standard Climate Change Scenarios and the ETCCDI Index. J. Korean Soc. Hazard Mitig. 19(7): 105-115. https://doi.org/10.9798/kosham.2019.19.7.105
  3. Kang, B. S., Lee, S. H., and Kwon, J. W. (2009). Properties Analysis of Climate Change on Hydrologic Variance in Korea Using Stationarity and Homogeneity Test. Proceeding of Korean Society of Civil Engineers. 2534-2637.
  4. Kim, B. S., Kang, K. S., and Seo, B. H. (1997). The Time Series Analysis of Annual Rainfall in Korea. Proceeding of Korea Water Resources Association. 280-285.
  5. Kim, T. R., Shin, H. J., Joo, K. W., and Heo, J. H. (2013). Trend Analysis of Extreme Hydrological Data Considering Skewness Degree. Proceeding of Korean Society of Civil Engineers. 1771-1773.
  6. Lee, J. J., Jang, J. Y., and Kwak, C. J. (2010). An Analysis of Temporal Characteristic Change for Various Hydrologic Weather Parameters (I) - On the Basic Statistic, Trend, Journal of Korea Water Resources Association. 43(4): 409-419. https://doi.org/10.3741/JKWRA.2010.43.4.409
  7. Park, C. S. and Lee, G. R. (2018). Analysis on Abnormal Trend in Climate Change of Weather Stations in Korea. Journal of Climate Research. 13(3): 179-195. https://doi.org/10.14383/cri.2018.13.3.179
  8. Park, J. H., Lee, G. S., Yang, J. S., and Kim, S. W. (2012). A Hydrometeorological Time Series Analysis of Geum River Watershed with GIS Data Considering Climate Change. Spatial Information Research. 20(3): 39-50. https://doi.org/10.12672/ksis.2012.20.3.039
  9. Ryu, Y. J., Shin, H. J., Kim, S. Y., and Heo, J. H. (2013). Power Test of Trend Analysis Using Simulation Experiment. Journal of Korea Water Resources Association. 46(3): 219-227. https://doi.org/10.3741/JKWRA.2013.46.3.219
  10. Song, H. K., Kim, S. H, Om, M. J., and Heo, J. H. (2015). Assessment of Spatial Trend and Stationary of Observed Rainfall by Considering Duration. Proceeding of Korean Society of Civil Engineers. 1-2.
  11. Yu, J. S., Kang, I. S., Park, J. I., and Shin, H. W. (1999). The Properties Analysis of Hydrology Time Series by the Use of Annual Rainfall Data. Journal of Research Institute of Industrial Technology. 15: 191-199.