Quantitative assessment of hydrological drought thresholds based on a single reservoir storage factor: Case studies of Hwacheon and Soyanggang Dams

Minsu Jeong; Hongjoon Shin; Jeonghwan Ahn; SeungOh Lee; Joo-Heon Lee

doi:10.3741/JKWRA.2026.59.7.635

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2026 Vol.59, Issue 7 Next Page

Quantitative assessment of hydrological drought thresholds based on a single reservoir storage factor: Case studies of Hwacheon and Soyanggang Dams 단일 저수량 인자 기반 수문학적 가뭄 임계치의 정량적 산정: 화천댐과 소양강댐을 대상으로

31 July 2026. pp. 635-649

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Abstract

Drought is categorized into meteorological, agricultural, and hydrological types, and is assessed by evaluating current water resource conditions relative to historical records using key variables for each sector. For drought assessment, the Standardized Precipitation Index (SPI) and the Standardized Reservoir Storage Index (SRSI)—which statistically normalize single variables such as precipitation and reservoir storage—are widely utilized. Conversely, in practical field operations, the Percent of Normal (PN), an intuitive ratio compared to average years, is primarily used. Such discrepancies in evaluation methods can lead to confusion when determining drought conditions and establishing response stages. This study proposes a methodology for the inverse calculation of a single-variable-based drought index into quantitative reservoir storage and provides a comparative analysis with indices used in practice. For hydrological drought assessment, the SRSI, which possesses statistical rigor, was applied. Quantitative reservoir storage volumes and ratios corresponding to drought thresholds were derived using the Inverse Gamma Cumulative Distribution Function (I-GCDF). In particular, this study analyzed the characteristics of the gamma distribution parameters for the Hwacheon and Soyanggang Dams to compare and analyze the seasonal sensitivity differences in critical storage rates based on dam operational purposes and scales. The results indicated that the quantified critical storage rates based on the drought index generally exceeded the current agricultural drought warning criteria (PN 70%). However, the findings also confirmed the necessity for a cautious approach due to significant numerical gaps (more than 10-20%) compared to practical standards. By providing a quantitative link between standardized drought indices and field operational indicators, this study seeks to improve the interpretability of drought analysis. The proposed quantitative thresholds are expected to support more objective and practical drought determination and decision-making, contributing to the establishment of future dam operation guidelines and systematic water resource management.

Keywords

Hydrological drought

SRSI

the Inverse Gamma Cumulative Distribution Function (I-GCDF)

Percent of Normal (PN)

Quantification of drought thresholds

가뭄은 기상, 농업, 수문학적 가뭄으로 구분되며, 각 분야별 주요인자를 활용하여 과거 기록대비 현재의 수자원 상태를 상대적으로 평가함으로써 판단된다. 가뭄평가를 위해 강수량과 저수량 등 단일인자를 통계적으로 표준화하는 SPI (Standardized Precipitation Index)와 SRSI (Standardized Reservoir Storage Index)가 널리 사용되고 있다. 반면, 실무현장에서는 직관적인 평년대비 비율인 PN (Percent of Normal)이 주로 활용되며, 이러한 평가 방식의 차이는 가뭄의 판단 및 가뭄 대응 단계 설정에 있어서 혼선을 야기할 수 있다. 본 연구에서는 단일인자 기반 가뭄지수에 대응하는 정량화된 저수량으로 역산(Inverse calculation)하는 방법론을 제시하고, 이를 실무에서 사용하는 지표와 비교 분석하였다. 수문학적인 가뭄평가에는 통계적 엄밀성을 갖춘 SRSI를 적용하였으며, 감마분포의 역산인 I-GCDF (Inverse Cumulative Distribution Function)를 이용하여 가뭄 임계값에 대응하는 저수량의 정량화 및 비율로 제시하였다. 특히, 화천댐과 소양강댐을 대상으로 감마분포 매개변수 특성과 댐의 운영 목적 및 규모에 따른 임계 저수율의 시기별 민감도 차이 등을 비교 분석하였다. 연구 결과, 가뭄지수 기반 정량화된 임계 저수율은 현행 농업 가뭄 경보 기준(PN 70%)을 상회하는 것으로 나타났지만, 동시에 실무기준과의 상당한 수치적 격차(10~20% 이상)에 따른 신중한 접근 필요성도 확인되었다. 본 연구는 표준화된 가뭄지수와 현장 실무 지표 간의 정량적 연결 고리를 제시함으로써 가뭄 분석의 해석 가능성을 향상시키고자 하였다. 제시된 정량적인 가뭄임계값은 객관적이고 실질적인 가뭄판단과 의사결정을 지원할 수 있으며, 향후 댐 운영 가이드라인 수립과 체계적인 수자원 관리에 기여할 것으로 판단된다.

키워드

수문학적 가뭄

SRSI

감마분포의 역산

평년대비 저수율

정량화된 가뭄 임계값

References

Gusyev, M.A., Hasegawa, A., Magome, J., Jaranilla-Sanchez, P.A., and Takeya, K. (2016). “Operationalizing the standardized reservoir supply index (SRSI) for the prediction of reservoir scarcity and water supply in drought management.” Journal of Hydrology, Vol. 542, pp. 452-463.

Gusyev, M.A., Hasegawa, A., Magome, J., Kuribayashi, D., Sawano, H., and Lee, S. (2015). “Drought assessment in the Pampanga River Basin, the Philippines. Part 1: A role of dam infrastructure in historical droughts.” Proceedings of the 21st International Congress on Modelling and Simulation (MODSIM 2015), Broadbeach, Queensland, Australia. doi: 10.13140/RG.2.1.4931.4321.

10.13140/RG.2.1.4931.4321

Hayes, M.J. (2006). “Drought indices.” Van Nostrand's Scientific Encyclopedia, Edited by Considine, G.D., John Wiley & Sons, Inc., Hoboken, NJ, U.S., p. 11. doi:10.1002/0471743984.vse8593.

10.1002/0471743984.vse8593

Jeong, M.S., Park, S.Y., Kim, Y.J., Yoon, H.C., and Lee, J. H. (2024). “Identification of propagation characteristics from meteorological drought to hydrological drought using daily drought indices and lagged correlations analysis.” Journal of Hydrology: Regional Studies, Vol. 55, 101939.

10.1016/j.ejrh.2024.101939

Ke, X., Wang, S., Zhou, M., and Ye, H. (2023). “New approaches on parameter estimation of the gamma distribution.” Mathematics, Vol. 11, No. 4, 927.

10.3390/math11040927

K-water (2018). The working manual of reservoir operations. pp. IV-537-IV-582.

Lee, K.M., Jung, M.K., Lee, J.H., and Kwon, H.H. (2024). “An analysis of the impact of recent severe droughts on dam water supply adjustment baseline: A case study of Juam Dam.” KSCE Journal of Civil and Environmental Engineering Research, Vol. 44, No. 6, pp. 807-815.

10.12652/KSCE.2024.44.6.0807

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

Nalbantis, I. (2008). “Evaluation of a hydrological drought index.” European Water, Vol. 23, No. 24, pp. 67-77.

10.1007/s11269-008-9305-1

Schilstra, M., Wang, W., van Oel, P.R., Wang, J., and Cheng, H. (2024). “The effects of reservoir storage and water use on the upstream-downstream drought propagation.” Journal of Hydrology, Vol. 631, 130668.

10.1016/j.jhydrol.2024.130668

Steinemann, A.C. (2003). “Drought indicators and triggers: A statistical comparison of common metrics.” Water Resources Research, Vol. 39, No. 9, 1257.

10.1111/j.1752-1688.2003.tb03704.x

Svoboda, M., LeComte, D., Hayes, M., Heim, R., Gleason, K., Angel, J., Rippey, B., Tinker, R., Palecki, M., Stooksbury, D., et al. (2002). “The drought monitor.” Bulletin of the American Meteorological Society, Vol. 83, No. 8, pp. 1181-1190.

10.1175/1520-0477-83.8.1181

Svoboda, M., Hayes, M., and Wood, D. (2012). Standardized precipitation index user guide. WMO-No. 1090, World Meteorological Organization, Geneva, Switzerland.

Svoboda, M.D., and Fuchs, B.A. (2016). Handbook of drought indicators and indices (Vol. 2).World Meteorological Organization, Geneva, Switzerland.

Willeke, G., Hosking, J.R.M., Wallis, J.R., and Guttman, N.B. (1994). The national drought atlas. Institute for Water Resources Report 94-NDS-4, U.S. Army Corps of Engineers, Alexandria, VA, U.S.

Won, K.J., and Chung, E.S. (2016). “Drought analysis of Cheongmicheon watershed using meteorological, agricultural and hydrological drought indices.” Journal of Korea Water Resources Association, KWRA, Vol. 49, No. 6, pp. 509-518.

10.3741/JKWRA.2016.49.6.509

World Meteorological Organization (WMO) (2012). Standardized precipitation index user guide. WMO-No. 1090, Geneva, Switzerland.

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 :7
Pages :635-649
Received Date : 2026-01-12
Revised Date : 2026-04-20
Accepted Date : 2026-05-06
DOI :https://doi.org/10.3741/JKWRA.2026.59.7.635

Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

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