All Issue

2026 Vol.59, Issue 10 Preview Page
Linking multiscale drought indices to observed agricultural impacts through temporal lead-lag analysis

가뭄지수와 관측 농업가뭄 영향자료 간 선행·지연 관계 분석

Jun-Yeong Seoa
,
Won-Ho Namb*
,
Young-Sik Munc
,
Min-Gi Jeond
,
Sang-Dan Kime
서 준영a
,
남 원호b*
,
문 영식c
,
전 민기d
,
김 상단e
aGraduate Student, School of Social Safety and Systems Engineering, Hankyong National University, Anseong, Korea
bProfessor, School of Social Safety and Systems Engineering, National Agricultural Water Research Center, Hankyong National University, Anseong, Korea
cSenior Researcher, National Agricultural Water Research Center, Hankyong National University, Anseong, Korea
dPostdoctoral Researcher, Water Resources and River Research Division, Korea Institute of Civil Engineering and Building Technology, Goyang, Korea
eProfessor, School of Division of Earth Environmental System Science, Pukyong National University, Busan, Korea
a한경국립대학교 사회안전시스템공학부 석사과정
b한경국립대학교 사회안전시스템공학부 교수
c한경국립대학교 국가농업용수연구센터 선임연구원
d한국건설기술연구원 수자원하천연구본부 박사후연구원
e국립부경대학교 지구환경시스템과학부 교수
*Corresponding Author
31 October 2026. pp. 1337-1346
PDF XML Citation
Abstract

Relationships between meteorological drought indices and observed agricultural impacts were evaluated in Seosan-si, Imsil-gun, and Goheung-gun, Republic of Korea. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were calculated at eight accumulation periods (1–36 months) and compared with paddy and upland drought forecasts and warnings, drought-affected area, crop wilting area, agricultural water supply, and the Soil Water Deficit Index (SWDI). Pearson correlations were evaluated using data pairs aligned at the lag applied to each index–indicator combination, with effective sample sizes and autocorrelation-adjusted p-values. Spearman correlations used the same pairs and lags for ordinal and zero-inflated indicators. Event-based lead–lag analysis used ±6-month and ±12-month search windows. Of 288 Pearson combinations, 100 were significant before adjustment and 70 remained significant afterward; thus, 30 lost significance. Pearson and Spearman signs agreed in 180 of 192 comparisons (93.8%). SWDI showed relatively strong positive correlations at 2-3-month accumulation periods. Twenty-five lag estimates reached -6 months; after window expansion, 19 shifted to -12 months and 23 were -10 months or less. The 36-month boundary-reaching rate was 66.5%. Results varied by region, indicator, accumulation period, and search boundary, and should support index selection rather than be interpreted as independent forecasting validation.

Keywords
Agricultural drought
Drought impact assessment
Drought propagation
Lead-lag analysis
SPI, SPEI

가뭄은 기상학적 강수 부족에서 시작하여 토양수분 감소, 작물 생육 저하 및 농업용수 부족으로 전파되며, 이 과정에서 다양한 시간적 지연(lead–lag)이 발생한다. 그러나 실제 농업가뭄 영향자료를 이용하여 기상학적 가뭄지수와의 선행·지연 관계를 체계적으로 정량화한 연구는 아직 제한적이다. 따라서 농업가뭄 영향평가를 위해서는 실제 영향자료를 활용하여 가뭄지수의 누적시간스케일별 민감도와 선행·지연 특성을 종합적으로 분석할 필요가 있다. 본 연구에서는 충청남도 서산시, 전라북도 임실군 및 전라남도 고흥군을 대상으로 기상학적 가뭄지수와 농업가뭄 영향지표 간의 시간적 관계를 분석하였다. 표준강수지수(SPI)와 표준강수증발산지수(SPEI)는 1~36개월의 8개 누적시간스케일에서 산정하고, 논·밭 가뭄 예·경보, 논 물마름 발생면적, 밭 시듦 발생면적, 농업용수 공급량 및 토양수분결핍지수(SWDI)와 비교하였다. Pearson 상관분석은 각 조합에 적용된 시차에 따라 정렬한 자료쌍을 이용하였으며, 유효표본수와 자기상관을 고려한 보정 유의확률을 산정하였다. 순서형 자료와 0의 발생 빈도가 높은(영과잉, zero-inflated) 자료에 대해서는 동일한 자료쌍과 시차를 이용한 Spearman 순위상관분석을 수행하였다. 이벤트 기반 선행·지연 분석은 ±6개월 및 ±12개월 탐색범위에서 수행하였다. 전체 288개 Pearson 조합 중 보정 전 100개, 보정 후 70개가 유의하여 30개 조합이 유의성을 상실하였다. Pearson과 Spearman 상관계수의 부호는 192개 조합 중 180개(93.8%)에서 일치하였다. SWDI는 2~3개월 누적시간스케일에서 상대적으로 높은 양의 상관관계를 보였다. 25개 시차가 -6개월 경계에 도달하였으며, 탐색범위를 확장한 결과 19개는 -12개월로 이동하였고, 23개는 -10개월 이하에서 최대 상관을 나타냈다. 또한 36개월 누적시간스케일의 경계 도달률은 66.5%로 나타났다. 본 연구 결과는 지역, 영향지표, 누적시간스케일 및 탐색범위에 따라 선행·지연 특성이 달라짐을 보여주었으며, 독립적인 예측성능 검증보다는 적절한 가뭄지수와 누적시간스케일을 선정하기 위한 기초자료로 활용될 수 있다.

키워드
농업가뭄
가뭄 영향평가
가뭄 전파
선행·지연 분석
표준강수지수
표준강수증발산지수
References
1

Bachmair, S., Svensson, C., Hannaford, J., Barker, L.J., and Stahl, K. (2016). “A quantitative analysis to objectively appraise drought indicators and model drought impacts.” Hydrology and Earth System Sciences, Vol. 20, No. 7, pp. 2589-2609.

10.5194/hess-20-2589-2016
2

Blauhut, V., Gudmundsson, L., and Stahl, K. (2016). “Estimating drought risk across Europe from reported drought impacts, drought indices, and vulnerability factors.” Hydrology and Earth System Sciences, Vol. 20, No. 7, pp. 2779-2800.

10.5194/hess-20-2779-2016
3

Guttman, N.B. (1999). “Accepting the standardized precipitation index: A calculation algorithm.” Journal of the American Water Resources Association, Vol. 35, No. 2, pp. 311-322.

10.1111/j.1752-1688.1999.tb03592.x
4

Intergovernmental Panel on Climate Change (IPCC). (2023). Climate change 2023: Synthesis report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, AR6 SYR, Geneva, Switzerland, pp. 35-115.

5

Jeon, M.G., Nam, W.H., Yang, M.H., Mun, Y.S., Hong, E.M., Ok, J.H., Hwang, S.A., and Hur, S.O. (2021). “Assessment of upland drought using soil moisture based on the water balance analysis.” Journal of the Korean Society of Agricultural Engineers, Vol. 63, No. 5, pp. 1-11.

10.5389/KSAE.2021.63.5.001
6

Kim, J., Kim, C., Park, S., and Han, H. (2025). “Correlation analysis between drought index and influencing factors considering temporal characteristics.” Journal of Wetlands Research, Vol. 27, No. 3, pp. 243-251.

10.17663/JWR.2025.27.3.243
7

Martínez-Fernández, J., González-Zamora, A., Sánchez, N., and Gumuzzio, A. (2015). “A soil water based index as a suitable agricultural drought indicator.” Journal of Hydrology, Vol. 522, pp. 265-273.

10.1016/j.jhydrol.2014.12.051
8

McKee, T.B., Doesken, N.J., and Kleist, J. (1993). “The relationship of drought frequency and duration to time scales.” Proceedings of the 8th Conference on Applied Climatology, AMS, Anaheim, CA, U.S., pp. 179-184.

9

Moon, J.Y., Park, C.Y., and Choi, Y.E. (2011). “Changes in the characteristics of summer rainfall caused by the regime shift in the Republic of Korea.” Journal of the Korean Geographical Society, Vol. 46, No. 3, pp. 277-290.

10.22776/KGS.2011.46.3.277
10

Nam, W.H., Kim, T.G., Hong, E.M., Hayes, M.J., and Svoboda, M.D. (2015). “Water supply risk assessment of agricultural reservoirs using irrigation vulnerability model and cluster analysis.” Journal of the Korean Society of Agricultural Engineers, Vol. 57, No. 1, pp. 59-67.

10.5389/KSAE.2015.57.1.059
11

Park, C.K., Lee, S., Yoon, H., and Kam, J. (2023). “Sub-seasonal to seasonal outlook of the 2022-23 southwestern Korea meteorological drought.” Environmental Research Letters, Vol. 18, No. 10, 104039.

10.1088/1748-9326/acfb27
12

Park, J.S., and Kim, K.T. (2009). “Evaluation of MODIS NDVI for drought monitoring: Focused on comparison of drought index.” Spatial Information Research, Vol. 17, No. 1, pp. 117-129.

13

Pyper, B.J., and Peterman, R.M. (1998). “Comparison of methods to account for autocorrelation in correlation analyses of fish data.” Canadian Journal of Fisheries and Aquatic Sciences, Vol. 55, No. 9, pp. 2127-2140.

10.1139/f98-104
14

Stagge, J.H., Tallaksen, L.M., Gudmundsson, L., Van Loon, A.F., and Stahl, K. (2015). “Candidate distributions for climatological drought indices (SPI and SPEI).” International Journal of Climatology, Vol. 35, No. 13, pp. 4027-4040.

10.1002/joc.4267
15

Sutanto, S.J., Vitolo, C., Di Napoli, C., D’Andrea, M., and Van Lanen, H.A.J. (2019). “Moving from drought hazard to impact forecasts.” Nature Communications, Vol. 10, 4945.

10.1038/s41467-019-12840-z31666523PMC6821769
16

Thornthwaite, C.W. (1948). “An approach toward a rational classification of climate.” Geographical Review, Vol. 38, No. 1, pp. 55-94.

10.2307/210739
17

Van Loon, A.F. (2015). “Hydrological drought explained.” WIREs Water, Vol. 2, No. 4, pp. 359-392.

10.1002/wat2.1085
18

Vicente-Serrano, S.M., Beguería, S., and López-Moreno, J.I. (2010). “A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index.” Journal of Climate, Vol. 23, No. 7, pp. 1696-1718.

10.1175/2009JCLI2909.1
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 59
  • No :10
  • Pages :1337-1346
  • Received Date : 2026-06-29
  • Revised Date : 2026-08-11
  • Accepted Date : 2026-08-13
  • DOI :https://doi.org/10.3741/JKWRA.2026.59.10.1337
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