KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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Correlation Analysis between Palmer Drought Severity Index(PDSI) and ENSO Indices

Palmer 가뭄지수(PDSI)와 ENSO 지수와의 상관성 분석

Kwon, Hyun-Han;Moon, Young-II
권현한;문영일

  • Published : 2005.09.30

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Abstract

In this study, we have investigated the relationship between the Palmer Drought Severity Index (PDSI) and the atmospheric indices. The relationship was examined by Cross Wavelet Transform and Multi-Channel Spectrum Analysis (MSSA). We evaluated the PDSI data estimated by Dai et al.(2004) with the rainfalls in Korea. The results showed a statistically strong correlation, and thus we can state that the PDSI data is adequate for this study. In the Cross Wavelet Analysis, the strong correlation between the ENSO(El Nino-Southern Oscillation) indices and the PDSI was shown in the low frequency; particularly this trend getting strong in the last 10 years. In order to extract the common component of low frequency, the MSSA was used. We evaluated the cross correlation analysis with two indices for determining the lag time and the correlation coefficient. The SOI (Southern Oscillation Index) showed 17 lag time and the weak correlation of 28%, but the Nino SSTs (Sea Surface Temperature) represented 8${\sim}$12 lag time and the strong correlation of 50${\sim}$70%. Therefore, the Nino SSTs among ENSO indices may be used as a predictor to forecast the low frequency of a drought event.

본 연구에서는 ENSO(El Nino-Southern Oscillation) 지수와 PDSI(Palmer Drought Severity Index)와의 저빈도 상관성 분석을 실시하였으며 이를 위하여 Cross Wavelet Transform과 MSSA(Multi-Channel Singular Spectrum Analysis)를 수행하였다. Dai 등(2004)이 추정한 우리나라에 PDSI자료와 실제 강수량과의 거동을 평가하였으며 통계적으로 강수량 자료와 높은 상관성을 확인하였고 연구를 위한 자료로서 적합성을 갖는 것으로 판단되었다. ENSO 지수와 PDSI와의 Cross Wavelet Transform을 실시하였고 대부분의 ENSO 지수와 PDSI 사이에서 강한 저빈도의 공통성분을 확인할 수 있었으며 특히 이러한 경향은 최근에 더욱 강하게 나타나고 있다. 두 개의 시계열을 대상으로 MSSA를 적용하여 저빈도의 공통주기를 추출하고 각 ENSO 지수와 PDSI와의 교차상관관계를 분석하였다. 분석결과 SOI(Southern Oscillation Index)는 17개월의 비교적 큰 지체특성과 28%의 낮은 상관성을 보이고, 반면에 Nino 지역의 SST(Sea Surface Temperature)는 8${\sim}$12개월의 지체시간과 50%${\sim}$70% 정도의 비교적 큰 상관관계를 나타내었다. 따라서 가뭄사상의 저빈도 주기를 예측하기 위한 예측자로서 ENSO 지수 중 Nino 지역의 SST를 활용할 수 있을 것이다.

Keywords

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