All Issue

2019 Vol.52, Issue 10 Preview Page

Research Article

Prospect of extreme precipitation in North Korea using an ensemble empirical mode decomposition method

앙상블 경험적 모드분해법을 활용한 북한지역 극한강수량 전망

Jinhong Junga
,
Dong-Hyeok Parkb
,
Jaehyun Ahnc*
정 진홍a
,
박 동혁b
,
안 재현c*
aGraduate Student, Department of Urban Infrastructure and Disaster Prevention Engineering, Seokyeong University
bResearcher, Urban Risk Management Research Center, Seokyeong University
cProfessor, Department of Civil & Architectural Engineering, Seokyeong University
a서경대학교 대학원 도시기반방재안전공학과 석사과정
b서경대학교 도시안전연구센터 박사 후 연구원
c서경대학교 이공대학 토목건축공학과 교수
* Corresponding Author
31 October 2019. pp. 671-680
PDF XML Citation
Abstract

Many researches illustrated that the magnitude and frequency of hydrological event would increase in the future due to changes of hydrological cycle components according to climate change. However, few studies performed quantitative analysis and evaluation of future rainfall in North Korea, where the damage caused by extreme precipitation is expected to occur as in South Korea. Therefore, this study predicted the extreme precipitation change of North Korea in the future (2020-2060) compared to the current (1981-2017) using stationary and nonstationary frequency analysis. This study conducted nonstationary frequency analysis considering the external factors (mean precipitation of JFM (Jan.-Mar.), AMJ (Apr.-Jun.), JAS (Jul.-Sept.), OND (Oct.-Dec.)) of the HadGEM2-AO model simulated according to the Representative Concentration Pathway (RCP) climate change scenarios. In order to select external factors that have a similar tendency with extreme rainfall events in North Korea, the maximum annual rainfall data was obtained by using the ensemble empirical mode decomposition (EEMD) method. Correlation analysis was performed between the extracted residue and the external factors. Considering selected external factors, nonstationary GEV model was constructed. In RCP4.5, four of the eight stations tended to decrease in future extreme precipitation compared to the present climate while three stations increased. On the other hand, in RCP8.5, two stations decreased while five stations increased.

Keywords
Climate change
EEMD
Non-stationary frequency analysis
Non-stationary GEV model

기후변화에 따른 수문순환 요소들의 변화로 인해 미래에는 전 세계적으로 수문사상의 규모 및 빈도가 증가할 것이라는 많은 선행연구들이 있다. 하지만 북한지역의 미래 강수량에 대한 정량적 연구와 평가는 미비한 실정이다. 북한지역 역시 우리나라와 마찬가지로 극한강수에 따른 피해가 발생될 것으로 예상되기 때문에 북한지역에 관한 연구는 지속적으로 진행되어야 한다. 따라서 본 연구에서는 정상성 및 비정상성 빈도해석을 통해 북한지역의 미래(2020-2060년) 극한강수를 산정하고 현재기후(1981-2017년)와 비교․분석하였다. 비정상성 빈도해석은 RCP기후변화시나리오에 따라 모의된 HadGEM2-AO모델의 외부인자(JFM(1-3월), AMJ(4-6월), JAS(7-9월), OND(10-12월)의 평균 강수량)를 고려하여 수행하였다. 북한지역 극치 강우 사상과 유사한 경향을 보이는 외부인자 선정을 위해 앙상블 경험적 모드분해법을 활용하여 연 최대 강우자료의 잔차를 추출하였다. 추출된 잔차와 외부인자 사이의 상관성분석을 실시하였다. 8개 지점(강계, 삼지연, 장진, 양덕, 함흥, 신포, 장전, 신계)에서 3개의 외부인자(AMJ, JAS, OND)가 경향이 있음을 확인하였다. 선정된 외부인자를 고려하여 비정상성 GEV모형을 구축하고 빈도해석을 수행하였다. 그 결과, RCP4.5에서는 8개 지점 중 4개 지점이 현재기후 대비 미래극한강수량이 감소하는 경향을 보였고 3개 지점이 증가하는 것으로 나타났다. 반면에 RCP8.5에서는 2개 지점이 감소하는 경향을 5개 지점이 증가하는 것으로 분석되었다.

키워드
기후변화
앙상블 경험적 모드분해법
비정상성 빈도해석
비정상성 GEV모형
References
1
Aryal, S. K., Bates, B. C., Campbell, E. P., Li, Y., Palmer, M. J., and Viney, N. R. (2009). "Characterizing and modeling temporal and spatial trends in rainfall extremes." Journal of Hydrometeorology, AMS, Vol. 10, No. 1, pp. 241-253.
10.1175/2008JHM1007.1
2
Coles, S. (2001). An introduction to statistical modeling of extreme values. Springer-Verlag, London, UK.
10.1007/978-1-4471-3675-0
3
Croux, C., and Dehon, C. (2010). "Influence functions of the spearman and Kendall correlation measures." Statistical Method and Applications, Vol. 19, No. 4, pp. 497-515.
10.1007/s10260-010-0142-z
4
Franks, S. W., and Kuczera, G. (2002). "Flood frequency analysis: Evidence and implications of secular climate variability, New South Wales." Water Resources Research, AGU, Vol. 38, No. 2, pp 432-439.
10.1029/2001WR000232
5
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C., and Liu, H. H. (1998). "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis." Proceedings of the Royal Society of London. Series A: Mathematical Physical and Engineering Sciences, Vol. 27, No. 2, pp. 903-995.
10.1098/rspa.1998.0193
6
Ihara, C., Kushnir, Y., Cane, M. A., and DE la Pena, V. H. (2007). "Indian summer monsoon rainfall and its link with ENSO and Indian Ocean climate indices." International Journal of Climatology, Vol. 27, No. 2, pp. 179-187.
10.1002/joc.1394
7
Intergovernmental Panel on Climate Change (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, p. 151.
10.1017/CBO9781107415416
8
Inha University (2007). Construction and transportation R&D report. Ministry of Construction & Transportation.
9
Jain, S., and Lall, U. (2000). "Magnitude and timing of annual maximum floods: Trends and large-scale climatic associations for the Blacksmith Fork river, Utah." Water Resources Research, Vol. 36, No. 12, pp. 331-343.
10.1029/2000WR900183
10
Jung, T. H., Kim, H. B., Kim, H. S., and Heo, J. H. (2019). "Selection of climate indices for nonstationary frequency analysis and estimation of rainfall quantile." Journal of the Korean Society of Civil Engineers, KSCE, Vol. 39, No. 1, pp. 165-174.
11
Kim, B. S., Lee, J. K., Kim, H. S., and Lee, J. W. (2011). "Non- stationary frequency analysis with climate variability using conditional Generalize Extreme Value distribution." Journal of Wetland Research, Vol. 13, No. 3, pp. 499-514.
12
Korean Meteorological Administration (2018). Climate change outlook report on the Korean Peninsula.
13
Koutsoyiannis, D. (2004). "Statistics of extremes and estimation of extreme rainfall: Ⅰ. Theoretical investigation." Hydrological Sciences Journal, Vol. 49, No. 4, pp. 575-590.
10.1623/hysj.49.4.575.54430
14
Kwon, H. H., and Lee, J. J. (2011). "Seasonal rainfall outlook of Nakdong River basin using nonstationary frequency analysis model and climate information." Journal of Korea Water Resources Association, KWRA, Vol. 44, No. 5, pp. 339-350.
10.3741/JKWRA.2011.44.5.339
15
Lee, O. J., Sim, I. K., and Kim, S. D. (2018). "Non-stationary frequency analysis of daily rainfall depth using climate variables." Journal of the Korean Society Hazard Mitigation, Vol. 18, No. 7, pp. 639-647.
10.9798/KOSHAM.2018.18.7.639
16
Li, J., and Tan, S. (2015). "Nonstationary flood frequency analysis for annual flood peak series, adopting climate indices and check dam index as covariates." Water Resources Management, Vol. 319, pp. 573-574.
10.1007/s11269-015-1133-5
17
Milly, P. C. D., Wetherald, R. T., Dunne, K. A., and Delworth, T. L. (2002). "Increasing risk of great floods in a changing climate." Nature, Vol. 415, No. 6871, pp. 514-517.
10.1038/415514a11823857
18
Myeong, S. J. (2018). "KDI review of the North Korean economy." Korea Development Institute, Vol. 20, No. 3, pp. 41-59.
19
Pizaro, G., and Lall. U. (2002). "El Nino and floods in the US west: What can we expect?" Eos Trans, AGU, Vol. 83, No. 32, pp. 349-352.
10.1029/2002EO000255
20
Sankarasubramanian, A., and Lall, U. (2003). "Flood quantiles in a changing climate: Seasonal forecast and causal relations." Water Resources Research, Vol. 39, No. 5, pp. 1134.
10.1029/2002WR001593
21
Solomon, S., Qin, D., Manning, M., Averyt, K., Marquis, M., Chen, Z., Tignor, M., and Miller, L. (2007). Climate change 2007: The physical science basis, Working group I contribution to the fourth assessment report of the IPCC, Cambridge university press. United Kingdom and New York, NY, USA.
22
Sung, J, H., Kim, B. S., Kang, H. S., and Cho, C. H. (2012). "Non-stationary frequency analysis for extreme precipitation based on representative concentration pathway (RCP) climate change scenario." Journal of Korean Society of Hazard Mitigation, Vol. 12, No. 2, pp. 231-244.
10.9798/KOSHAM.2012.12.2.231
23
Thiombiano A. N., St-Hilaire, A., El, Adlouni, S., and Ouarda, T. B. J. (2018). "Nonlinear response of precipitation to climate indices using a non-stationary Poisson-generalized Pareto model: case study of southern Canada." International Journal of Climatology, Vol. 38, No. 1, pp. 878-888.
10.1002/joc.5415
24
Wu, Z., and Huang, N. E. (2009). "Ensemble empirical mode decomposition: a noise-assisted data analysis method." Advances in Adaptive Data Analysis, Vol. 1, No. 1, pp. 1-41.
10.1142/S1793536909000047
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 52
  • No :10
  • Pages :671-680
  • Received Date : 2019-08-21
  • Revised Date : 2019-09-15
  • Accepted Date : 2019-09-15
  • DOI :https://doi.org/10.3741/JKWRA.2019.52.10.671
Journal Informaiton Journal of Korea Water Resources Association Journal of Korea Water Resources Association
Online Submission
Archives
  • scopus
  • KCI-수자원
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close