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2026 Vol.59, Issue 2 Preview Page

Research Article

Evaluation of GR4J streamflow simulations incorporating snow accumulation and snowmelt

적설 및 융설 영향을 반영한 GR4J 모형의 유출 모의 평가

Seo Gyun Leea
,
Moon Hyung Parkb
,
Subin Kangc
,
Hyun Han Kwond*
이 서균a
,
박 문형b
,
강 수빈c
,
권 현한d*
aMaster Course, Department of Civil and Environmental Engineering, Sejong University, Seoul, Korea
bResarch Fellow, Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Korea
cPh.D, Department of Civil and Environmental Engineering, Sejong University, Seoul, Korea
dProfessor, Department of Civil & Environmental Engineering, Sejong University, Seoul, Korea
a세종대학교 건설환경공학과 석사과정
b한국건설기술연구원 수자원하천연구본부 연구위원
c세종대학교 건설환경공학과 박사 후 연구원
d세종대학교 건설환경공학과 교수
*Corresponding Author
28 February 2026. pp. 187-199
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Abstract

This study aims to evaluate and improve the reproducibility of winter–spring (December–April) streamflow using a GR4J-SN model that explicitly accounts for snow accumulation and melt. Precipitation, air temperature, and dam‐inflow data were used to estimate seasonally varying parameter sets for three regimes—December–February, March–April, and May–November—via a multi-objective function combining the Kling–Gupta efficiency (KGE) with RMSE computed separately for low and high flows. Model skill was assessed for the full period and by month using KGE, Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), RMSE, and PBIAS. In addition, standardized anomaly plots and conditional probability analysis were employed to examine temperature–runoff response characteristics. Results show that in March–April, when snowmelt strongly influences runoff, KGE improved from 0.400 and 0.471(GR4J) to 0.754 and 0.767(GR4J-SN) and NSE from 0.145 and 0.576 to 0.562 and 0.578, indicating clear gains in reproducing both peak flows and low-flow conditions. The systematic overestimation of winter flows was also alleviated, while annual KGE and NSE remained ≥0.90, demonstrating that the December–April improvements were achieved without degrading performance in other seasons. These findings suggest that a snow-aware GR4J-SN model provides sufficient reliability for dam-inflow simulation and offers practical support for spring flow forecasting and water-resources management in snow-affected mountainous basins.

Keywords
Snowmelt
Snowpack
Snowfall
GR4J
GR4J-SN

본 연구의 목적은 융·적설 과정을 고려한 GR4J-SN 모형을 활용하여 겨울-봄(12~4월) 유량 재현성을 평가 및 개선하는 데 있다. 이를 위해 강수, 기온, 댐 유입량 자료를 이용하여 KGE와 저·고유량을 분리한 RMSE로 구성된 다목적 함수를 활용해 12~2월, 3~4월, 5~11월 구간별 매개변수를 추정하였다. 모형 성능 평가는 전체 기간과 월별 KGE, NSE, CC, RMSE, PBIAS를 통해 수행하였으며, 표준화 아노말리 및 조건부 확률 분석을 통해 기온 변화에 따른 유량 반응 특성을 검토하였다. 그 결과, 3~4월과 같이 융설이 유출에 주요하게 작용하는 시기에서 기존 GR4J 모형 대비 KGE가 0.400, 0.471에서 0.754, 0.767로, NSE가 0.145, 0.576에서 0.562, 0.578로 향상되었으며, 봄철 첨두유량 및 저유량 구간의 재현성이 뚜렷이 개선되었다. 또한 겨울의 유량 과대 추정 경향이 완화되고, 연간 KGE 및 NSE는 0.9 이상으로 유지되어 타 계절의 모의 성능 저하 없이 12월~4월 유량 성능을 개선하였다. 이러한 결과는 융·적설 영향을 반영한 GR4J-SN 모형이 댐 유입량 모의에 충분한 적용성을 가지며, 향후 봄철 유량 예측 및 산지 유역 수자원 관리에 중요한 정보를 제공할 수 있을 것으로 기대된다.

키워드
융설
적설
강설
GR4J
GR4J-SN
References
1

Choi, H.I. (2024). “New objective functions for streamflow calibrations to specific model applications.” Journal of Hydrology, Vol. 644, 132109.

10.1016/j.jhydrol.2024.132109
2

Coello, C.A.C., Pulido, G.T., and Lechuga, M.S. (2004). “Handling multiple objectives with particle swarm optimization.” IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, pp. 256-279.

10.1109/TEVC.2004.826067
3

Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, pp. 182-197.

10.1109/4235.996017
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
5

Hao, Y., Sun, F., Wang, H., Liu, W., Shen, Y.J., Li, Z., and Hu, S. (2022). “Understanding climate-induced changes of snow hydrological processes in the Kaidu River Basin through the CemaNeige-GR6J model.” Catena, Vol. 212, 106082.

10.1016/j.catena.2022.106082
6

Im, S.S., Yoo, D.G., and Kim, J.H. (2012). “Improvement of GR4J model applying soil moisture accounting process and its application in Korea Basin.” Journal of the Korean Society of Hazard Mitigation, Vol. 12, No. 3, pp. 255-262.

10.9798/KOSHAM.2012.12.3.255
7

Jenicek, M., and Ledvinka, O. (2020). “Importance of snowmelt contribution to seasonal runoff and summer low flows in Czechia. Hydrology and Earth System Sciences, Vol. 24, No. 7, pp. 3475-3491.

10.5194/hess-24-3475-2020
8

Jo, Y., and Jung, K. (2024). “Study on data preprocessing methods for considering snow accumulation and snow melt in dam inflow prediction using machine learning & deep learning models.” Journal of Korea Water Resources Association, Vol. 57, No. 1, pp. 35-44.

10.3741/JKWRA.2024.57.1.35
9

Kim, D.G., Jeong, J.U., Park, J.H., and Park, C.G. (2007). “Long-term runoff simulation in consideration of snow pack and snow melt.” Journal of Korean Society of Water and Wastewater, Vol. 21, No. 3, pp. 265-272.

10

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
11

Kling, H., Fuchs, M., and Paulin, M. (2012). “Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios.” Journal of Hydrology, Vol. 424, pp. 264-277.

10.1016/j.jhydrol.2012.01.011
12

Lee, Y.-R., Yu, J.-U., Kim, K., and Kwon, H.-H. (2023). “A development of hydrological model calibration technique considering seasonality via regional sensitivity analysis.” KSCE Journal of Civil and Environmental Engineering Research, Vol. 43, No. 3, pp. 337-352.

10.12652/KSCE.2023.43.3.0337
13

Muñoz-Castro, E., Anderson, B.J., Astagneau, P.C., Swain, D.L., Mendoza, P.A., and Brunner, M.I. (2025). “How well do hydrological models simulate streamflow extremes and drought-to-flood transitions?” EGUsphere, Vol. 2025, pp. 1-37.

10.5194/egusphere-2025-781-supplement
14

Nepal, S., Chen, J., Penton, D.J., Neumann, L.E., Zheng, H., and Wahid, S. (2017). “Spatial GR4J conceptualization of the Tamor glaciated alpine catchment in Eastern Nepal: evaluation of GR4JSG against streamflow and MODIS snow extent.” Hydrological Processes, Vol. 31, No. 1, pp. 51-68.

10.1002/hyp.10962
15

Nyamgerel, Y., Jung, H., Koh, D.-C., Ko, K.-S., and Lee, J. (2022). “Variability in soil moisture by natural and artificial snow: A case study in Mt. Balwang area, gangwon-do, South Korea.” Frontiers in Earth Science,Vol. 9, 786356.

10.3389/feart.2021.786356
16

Perrin, C., Michel, C., and Andréassian, 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
17

Quintero, F., Krajewski, W.F., Seo, B.-C., and Mantilla, R. (2020). “Improvement and evaluation of the Iowa Flood Center Hillslope Link Model (HLM) by calibration-free approach.” Journal of Hydrology, Vol. 584, 124686.

10.1016/j.jhydrol.2020.124686
18

Staudinger, M., Herzog, A., Loritz, R., Houska, T., Pool, S., Spieler, D., Wagner, P.D., Mai, J., Kiesel, J., and Thober, S. (2025). “How well do hydrological models learn from limited discharge data? A comparison of process-and data-driven models.” EGUsphere, Vol. 2025, pp. 1-34.

10.5194/egusphere-2025-1076
19

Valéry, A., Andréassian, V., and Perrin, C. (2014). “‘As simple as possible but not simpler’: What is useful in a temperature-based snow-accounting routine? Part 2–Sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments.” Journal of Hydrology, Vol. 517, pp. 1176-1187.

10.1016/j.jhydrol.2014.04.058
20

Xu, M., Sun, Y., Wang, H., Qi, P., Peng, Z., Wu, Y., and Zhang, G. (2024). “Altitude characteristics in the response of rain-on-snow flood risk to future climate change in a high-latitude water tower.” Journal of Environmental Management, Vol. 369, 122292.

10.1016/j.jenvman.2024.122292
21

Yu, J.-U., Park, M.-H., Kim, J.-G., and Kwon, H.-H. (2021). “Evaluation of conceptual rainfall-runoff models for different flow regimes and development of ensemble model.” Journal of Korea Water Resources Association, Vol. 54, No. 2, pp. 105-119.

10.3741/JKWRA.2021.54.2.105
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 :2
  • Pages :187-199
  • Received Date : 2025-11-28
  • Revised Date : 2025-12-24
  • Accepted Date : 2026-01-07
  • DOI :https://doi.org/10.3741/JKWRA.2026.59.2.187
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