Evaluating the impact of rainfall-runoff model modifications on ensemble inflow forecast accuracy: A case study of the Seomjin River Basin

Shinuk Kang; Jinhyeog Park; Wansik Yu; Hyungu Choi

doi:10.3741/JKWRA.2026.59.9.1085

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2026 Vol.59, Issue 9 Preview Page Next Page

Evaluating the impact of rainfall-runoff model modifications on ensemble inflow forecast accuracy: A case study of the Seomjin River Basin 강우-유출 모형 변경에 따른 앙상블 유입량전망 정확도 향상 효과 평가: 섬진강권역 사례

30 September 2026. pp. 1085-1097

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Abstract

To cope with droughts exacerbated by climate change, this study evaluated the performance of Ensemble Streamflow Prediction (ESP) utilizing the GR4J hydrological model, known for its simple and stable structure. Based on an analysis of five dams in the Seomjin River basin (Seomjingang, Juam, Juam Regulation, Jangheung, and Pyeongnim Dams) that experienced severe drought in 2022, the GR4J model demonstrated excellent reproducibility by precisely simulating the basin's runoff responses using only a small number of parameters. In particular, compared to the conventional Tank model, the forecasting error (RMSE) was reduced by approximately 8 to 12%, and prediction reliability was significantly improved during low-flow periods without rainfall. In conclusion, the advanced GR4J-based ESP forecasting system reduces the uncertainty of drought predictions and enhances accuracy, which is expected to greatly contribute to the proactive water supply management of multi-purpose dams in the future.

Keywords

Rainfall-runoff model

Ensemble streamflow prediction

GR4J

Seomjin River Basin

Water supply management

본 연구는 기후변화로 인한 가뭄에 대응하기 위해, 구조가 간결하고 안정적인 GR4J 수문 모형을 활용한 앙상블 유량전망(ESP)의 성능을 평가하였다. 2022년 극심한 가뭄을 겪은 섬진강 권역 5개 댐(섬진강댐, 주암댐, 주암조절지댐, 장흥댐, 평림댐)을 대상으로 분석한 결과, GR4J 모형은 적은 매개변수만으로도 유역의 유출 응답을 정교하게 모사하며 우수한 재현성을 보였다. 특히 기존에 사용되던 Tank 모형에 비해 전망 오차(RMSE)를 약 8~12% 줄였으며, 비가 오지 않는 저유량 구간에서의 예측 신뢰도를 크게 향상시키는 것을 확인하였다. 결론적으로 고도화된 GR4J 기반의 ESP 전망 체계는 가뭄 예측의 불확실성을 낮추고 정확도를 높여, 향후 다목적댐의 선제적인 용수 공급 관리에 크게 기여할 수 있을 것으로 기대된다.

키워드

강우-유출 모형

앙상블 유량전망

GR4J

섬진강권역

용수공급관리

References

Day, G.N. (1985). “Extended streamflow forecasting using NWSRFS.” Journal of Water Resources Planning and Management, Vol. 111, No. 2, pp. 157-170.

10.1061/(ASCE)0733-9496(1985)111:2(157)

Duan, Q. (1991). A global optimization strategy for efficient and effective calibration of hydrologic models. Ph.D. dissertation, University of Arizona, Tucson, AZ, U.S.

Duan, Q., Sorooshian, S., and Gupta, V.K. (1994). “Optimal use of the SCE-UA global optimization method for calibrating watershed models.” Journal of Hydrology, Vol. 158, No. 3-4, pp. 265-284.

10.1016/0022-1694(94)90057-4

Gupta, H.V., Kling, H., Yilmaz, K.K., and Martinez, G.F. (2009). “Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling.” Journal of Hydrology, Vol. 377, No. 1-2, pp. 80-91.

10.1016/j.jhydrol.2009.08.003

Kapoor, A., and Chandra, R. (2026), “QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification.” Journal of Hydrology, Vol. 664, 134434.

10.1016/j.jhydrol.2025.134434

Kapoor, A., Pathiraja, S., Marshall, L., and Chandra, R. (2023). “DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling.” Environmental Modelling & Software, Vol. 169, 105831.

10.1016/j.envsoft.2023.105831

Kim, J.G., Uranchimeg, S., Kim, T.J., and Kwon, H.H. (2021). “Regionalization of rainfall-runoff model parameters based on the correlation of regional characteristic factors.” Journal of Korea Water Resources Association, Vol. 54, No. 11, pp. 955-968.

10.3741/JKWRA.2021.54.11.955

Kim, Y.I., Seo, S.B., and Kim, Y.O. (2018). “Development of a hybrid regionalization model for estimation of hydrological model parameters for ungauged watersheds.” Journal of Korea Water Resources Association, Vol. 51, No. 8, pp. 677-686.

10.3741/JKWRA.2018.51.8.677

Lee, G.R., Woo, D.K., and Noh, S.J. (2024). “Comparative assessment of sequential data assimilation-based streamflow predictions using semi-distributed and lumped GR4J hydrologic models: A case study of the Namgang Dam basin.” Journal of Korea Water Resources Association, Vol. 57, No. 9, pp. 585-598.

10.3741/JKWRA.2024.57.9.585

Legates, D.R., and McCabe, G.J. (1999). “Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation.” Water Resources Research, Vol. 35, No. 1, pp. 233-241.

10.1029/1998WR900018

Mathias, S.A., Thébault, C., and Ireson, A. (2026). “A theoretical appraisal of the GR4J rainfall-runoff modelling framework.” Journal of Hydrology, Vol. 664, 134393.

10.1016/j.jhydrol.2025.134393

McCabe, G.J., and Markstrom, S.L. (2007). A monthly water-balance model driven by a graphical user interface, Open-File Report 2007-1088, U.S. Geological Survey, Reston, VA, U.S.

10.3133/ofr20071088

Oudin, L., Andréassian, V., Perrin, C., Michel, C., and Le Moine, N. (2008). “Spatial proximity, physical similarity, regression and un-gaged catchments: A comparison of regionalization approachesbased on 913 French catchments.” Water Resources Research, Vol. 44, W03413.

10.1029/2007WR006240

Perrin, C., Michel, C., and Andreassian, V. (2003). “Improvement of a parsimonious model for streamflow simulation.” Journal of Hydrology, Vol. 279, No. 1-4, pp. 275-289.

10.1016/S0022-1694(03)00225-7

Pushpalatha, R., Perrin, C., Le Moine, N., and Andréassian, V. (2011). “A review of efficiency criteria suitable for evaluating low-flow simulations.” Journal of Hydrology, Vol. 420, No. 3, pp. 171-182.

10.1016/j.jhydrol.2011.11.055

Sun, M., Yan, B., Zhou, X., Chang, J., and Du, S. (2025). “Hybrid-GR4J: A hybrid hydrological model integrating GR4J and deep learning.” Environmental Modelling & Software, Vol. 193, 106636.

10.1016/j.envsoft.2025.106636

Willmott, C.J., and Matsuura, K. (2005). “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance.” Climate Research, Vol. 30, No. 1, pp. 79-82.

10.3354/cr030079

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 :9
Pages :1085-1097
Received Date : 2026-07-08
Revised Date : 2026-07-13
Accepted Date : 2026-07-14
DOI :https://doi.org/10.3741/JKWRA.2026.59.9.1085

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

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