All Issue

2024 Vol.57, Issue 9 Preview Page

Research Article

Comparative assessment of sequential data assimilation-based streamflow predictions using semi-distributed and lumped GR4J hydrologic models: a case study of Namgang Dam basin

준분포형 및 집중형 GR4J 수문모형을 활용한 순차자료동화 기반 유량 예측 특성 비교: 남강댐 유역 사례

Garim Leeab
,
Dong Kook Wooc
,
Seong Jin Nohd*
이 가림ab
,
우 동국c
,
노 성진d*
aMaster Student, Department of Civil Engineering, Kumoh National Institute of Technology, Gumi, Korea
bResearcher, Han River Environment Research Center, National Institute of Environmental Research, Yangpyeong, Korea
cAssistant Professor, Department of Civil Engineering, Keimyung University, Daegu, Korea
dAssociate Professor, Department of Civil Engineering, Kumoh National Institute of Technology, Gumi, Korea
a금오공과대학교 토목공학과 석사과정
b국립환경과학원 한강물환경연구소 연구원
c계명대학교 토목공학전공 조교수
d금오공과대학교 토목공학과 부교수
*Corresponding Author
30 September 2024. pp. 585-598
PDF XML Citation
Abstract

To mitigate natural disasters and efficiently manage water resources, it is essential to enhance hydrologic prediction while reducing model structural uncertainties. This study analyzed the impact of lumped and semi-distributed GR4J model structures on simulation performance and evaluated uncertainties with and without data assimilation techniques. The Ensemble Kalman Filter (EnKF) and Particle Filter (PF) methods were applied to the Namgang Dam basin. Simulation results showed that the Kling-Gupta efficiency (KGE) index was 0.749 for the lumped model and 0.831 for the semi-distributed model, indicating improved performance in semi-distributed modeling by 11.0%. Additionally, the impact of uncertainties in meteorological forcings (precipitation and potential evapotranspiration) on data assimilation performance was analyzed. Optimal uncertainty conditions varied by data assimilation method for the lumped model and by sub-basin for the semi-distributed model. Moreover, reducing the calibration period length during data assimilation led to decreased simulation performance. Overall, the semi-distributed model showed improved flood simulation performance when combined with data assimilation compared to the lumped model. Selecting appropriate hyper-parameters and calibration periods according to the model structure was crucial for achieving optimal performance.

Keywords
Hydrologic Model structure
Data assimilation
Ensemble Kalman filter
Particle filter
Uncertainty analysis

자연재해를 사전에 대비하고 효율적인 수자원 관리를 하기 위해서는 수문모형의 구조적 특성이 예측 성능에 미치는 영향을 파악하고 불확실성을 최소화하여 수문예측의 정확도를 향상시키는 것이 중요하다. 본 연구에서는 모형의 구조가 상이한 준분포형과 집중형 GR4J 모형을 순차 자료동화 기법과 연계 적용하여 하천 유량 모의성능에 미치는 영향을 분석하였다. 이를 위해 남강댐 유역에 대해 앙상블 칼만 필터(Ensemble Kalman Filter, EnKF)와 파티클 필터(Particle Filter, PF) 기법을 적용하였다. 모의결과, 두 수문모형의 Kling-Gupta efficiency (KGE) 지표는 파티클 필터 적용시 0.749(집중형), 0.831(준분포형)로, 집중형 모형 보다 준분포형 모형에서 0.082(11.0%) 향상되었다. 또한, 자료동화와 관련된 하이퍼-매개변수 중 기상강제력(강수, 잠재증발산) 불확실성이 모의성능에 미치는 영향을 분석하였다. 집중형 모형은 수문자료동화 기법에 따라, 준분포형 모형은 각 하위유역에 따라 최적 성능을 얻기 위한 불확실성 조건이 상이하였다. 한편, 자료동화 성능에 보정 및 검정기간의 비율이 미치는 영향을 평가한 결과, 앙상블 칼만 필터는 보정기간이 짧아질수록 자료동화 성능은 감소하였으나, 파티클 필터는 상대적으로 영향을 적게 받았다. 또한, 자료동화의 최적성능을 얻기 위해서는 모형 구조에 따른 적절한 하이퍼-매개변수와 보정기간 선정이 중요함을 확인하였다.

키워드
수문모형 구조
자료동화
앙상블 칼만 필터
파티클 필터
불확실성 분석
References
1

Alfieri, L., Salamon, P., Pappenberger, F., Wetterhall, F., and Thielen, J. (2012). "Operational early warning systems for water-related hazards in Europe." Environmental Science & Policy, Vol. 21, pp. 35-49.

10.1016/j.envsci.2012.01.008
2

Aubert, D., Loumagne, C., and Oudin, L. (2003). "Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall - runoff model." Journal of Hydrology, Vol. 280, No. 1-4, pp. 145-161.

10.1016/S0022-1694(03)00229-4
3

Choi, J.-H., and Kim, S.-D. (2021). "Estimating time-varying parameters for monthly water balance model using particle filter: assimilation of stream flow data." Journal of Korea Water Resources Association, Vol. 54, No. 6, pp. 365-379.

10.3741/JKWRA.2021.54.6.365
4

Cloke, H.L., and Pappenberger, F. (2009). "Ensemble flood forecasting: A review." Journal of Hydrology, Vol. 375, No. 3-4, pp. 613-626.

10.1016/j.jhydrol.2009.06.005
5

Ercolani, G., and Castelli, F. (2017). "Variational assimilation of streamflow data in distributed flood forecasting." Water Resources Research, Vol. 53, No. 1, pp. 158-183.

10.1002/2016WR019208
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 Korean Society of Hazard Mitigation, Vol. 12, No. 3, pp. 255-262.

10.9798/KOSHAM.2012.12.3.255
7

Leach, J.M., and Coulibaly, P. (2019). "An extension of data assimilation into the short-term hydrologic forecast for improved prediction reliability." Advances in Water Resources, Vol. 134, 103443.

10.1016/j.advwatres.2019.103443
8

Lee G. (2024). Comparative assessment of sequential data assimilation-based streamflow predictions semi-distributed and lumped GR4J hydrologic models. Master Thesis, Kumoh National Institute of Technology.

9

Lee, G., Lee, S., Kim, B., Woo, D.K., and Noh, S.J. (2022). "Comparative assessment and uncertainty analysis of ensemble-based hydrologic data assimilation using airGRdatassim." Journal of Korea Water Resources Association, Vol. 55, No. 10, pp. 761- 774.

10

Lee, H., Seo, D.-J., Liu, Y., Koren, V., McKee, P., and Corby, R. (2012). "Variational assimilation of streamflow into operational distributed hydrologic models: Effect of spatiotemporal adjustment scale." Hydrology and Earth System Sciences, Copernicus Publications, Vol. 9, No. 1, pp. 93-138.

10.5194/hess-16-2233-2012
11

Liu, Y., Wang, W., Hu, Y., and Cui, W. (2016). "Improving the distributed hydrological model performance in upper Huai River basin: Using streamflow observations to update the basin states via the Ensemble Kalman Filter." Advances in Meteorology, Vol. 2016, No. 1, pp. 1-14.

10.1155/2016/4921616
12

Nayak, A.K., Biswal, B., and Sudheer, K.P. (2021). "Role of hydrological model structure in the assimilation of soil moisture for streamflow prediction." Journal of Hydrology, Vol. 598, 126465.

10.1016/j.jhydrol.2021.126465
13

Noh, S.J., Lee, G., Kim, B., Jo, J., and Woo, D.K. (2023). "Climate change impact analysis on water supply reliability and flood risk using combined rainfall-runoff and reservoir operation modeling: Hapcheon-Dam catchment case." Journal of Korea Water Resources Association, Vol. 56, No. 11, pp. 765-774.

14

Noh, S.J., Rakovec, O., Weerts, A.H., and Tachikawa, Y. (2014). "On noise specification in data assimilation schemes for improved flood forecasting using distributed hydrological models." Journal of Hydrology, Vol. 519, Part D, pp. 2707- 2721.

10.1016/j.jhydrol.2014.07.049
15

Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil, F., and Loumagne, C. (2005). "Which potential evapotranspiration input for a lumped rainfall - runoff model?: Part 2 - Towards a simple and efficient potential evapotranspiration model for rainfall - runoff modelling." Journal of Hydrology, Vol. 303, No. 1, pp. 290-306.

10.1016/j.jhydrol.2004.08.026
16

Oudin, L., Moulin, L., Bendjoudi, H., and Ribstein, P. (2010). "Estimating potential evapotranspiration without continuous daily data: Possible errors and impact on water balance simulations." Hydrological Sciences Journal, Taylor & Francis, Vol. 55, No. 2, pp. 209-222.

10.1080/02626660903546118
17

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
18

Rakovec, O., Weerts, A.H., Hazenberg, P., Torfs, P.J.J.F., and Uijlenhoet, R. (2012). "State updating of a distributed hydrological model with Ensemble Kalman Filtering: Effects of updating frequency and observation network density on forecast accuracy." Hydrology and Earth System Sciences, Copernicus Publications, Vol. 16, No. 9, pp. 3435-3449.

10.5194/hess-16-3435-2012
19

Ricci, S., Piacentini, A., Thual, O., Le Pape, E., and Jonville, G. (2011). "Correction of upstream flow and hydraulic state with data assimilation in the context of flood forecasting." Hydrology and Earth System Sciences, Copernicus Publications, Vol. 15, No. 11, pp. 3555-3575.

10.5194/hess-15-3555-2011
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 57
  • No :9
  • Pages :585-598
  • Received Date : 2024-07-04
  • Revised Date : 2024-08-22
  • Accepted Date : 2024-08-27
  • DOI :https://doi.org/10.3741/JKWRA.2024.57.9.585
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