Comparative assessment and uncertainty analysis of ensemble-based hydrologic data assimilation using airGRdatassim

Garim Lee; Songhee Lee; Bomi Kim; Dong Kook Woo; Seong Jin Noh

doi:10.3741/JKWRA.2022.55.10.761

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2022 Vol.55, Issue 10 Preview Page Next Page

Research Article

Comparative assessment and uncertainty analysis of ensemble-based hydrologic data assimilation using airGRdatassim airGRdatassim을 이용한 앙상블 기반 수문자료동화 기법의 비교 및 불확실성 평가

31 October 2022. pp. 761-774

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Abstract

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

Keywords

Data assimilation

Ensemble Kalman filter

Particle filter

airGRdatassim

Uncertainty analysis

가뭄과 홍수의 예측, 기후변화가 유역 유출량, 더 나아가 수질 및 생태계에 미치는 영향의 정확한 분석을 위해서는 수문 모의 과정의 불확실성을 정량화하고 최소화하기 위한 노력이 필요하다. 수문자료동화는 수문모형의 상태량이나 매개변수를 갱신(update)하여 모의 초기 조건의 가장 가능성 있는 추정치를 생성하는 기법으로, 실시간 관측 정보를 이용하여 예측 정확도를 향상시킬 수 있는 방법이다. 본 연구에서는 airGRdatassim 모형을 이용하여 앙상블 기반 순차 자료동화 기법인 앙상블 칼만 필터와 파티클 필터로 용담댐 유역에 대해 일 유출을 모의하고, 자료동화 기법별 특성을 비교 및 분석하였다. 모의 결과, Kling-Gupta efficiency (KGE) 지표가 자료동화 적용 전 0.799에서 앙상블 칼만 필터와 파티클 필터 적용시 각각 0.826, 0.933으로 향상되었다. 또한 기상 강제력 노이즈의 범위, 갱신 대상 상태량 설정, 앙상블 수 등 수문자료동화의 설정과 관련된 하이퍼-매개변수(hyper-parameter)의 불확실성이 모의 예측 성능에 미치는 영향을 분석하였다. 강수 및 잠재 증발산 강제력의 오차 범위에 대한 민감도 분석 결과, 모든 모의 범위에서 파티클 필터가 앙상블 칼만 필터보다 예측 성능이 우수하였다. 파티클 필터는 기상 강제력 오차 크기가 작을수록 모의 성능이 향상되었으며, 앙상블 칼만 필터는 상대적으로 오차가 큰 경우 최적 성능이 확인되었다. 한편, 자료동화시 갱신되는 상태량의 종류를 줄일수록 자료동화에 의한 모의 성능은 감소하였다. 본 연구의 모의 실험 결과는 앙상블 자료동화를 이용하여 일 유출 모의 정확도 향상이 가능하지만, 최적 성능을 발휘하기 위해서는 수문자료동화 기법별 하이퍼-매개변수의 적정한 조정이 필요함을 함의한다.

키워드

자료동화

앙상블 칼만 필터

파티클 필터

airGRdatassim

불확실성 분석

References

Adeyeri, O.E., Laux, P., Arnault, J., Lawin, A.E., and Kunstmann, H. (2020). "Conceptual hydrological model calibration using multi-objective optimization techniques over the transboundary Komadugu-Yobe basin, Lake Chad Area, West Africa." Journal of Hydrology: Regional Studies, Vol. 27, 100655. 10.1016/j.ejrh.2019.100655

Arulampalam, M.S., Maskell, S., Gordon, N., and Clapp, T. (2002). "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking." IEEE Transactions on Signal Processing, Vol. 50, No. 2, pp. 174-188. 10.1109/78.978374

Blöschl, G., Bierkens, M.F.P., Chambel, A., Cudennec, C., Destouni, G., Fiori, A., Kirchner, J.W., McDonnell, J.J., Savenije, H.H.G., Sivapalan, M. et al. (2019). "Twenty-three unsolved problems in hydrology (UPH) - a community perspective." Hydrological Sciences Journal, Vol. 64, No. 10, pp. 1141-1158. 10.1080/02626667.2019.1620507

Boucher, M.-A., Quilty, J., and Adamowski, J. (2020). "Data assimilation for streamflow forecasting using extreme learning machines and multilayer perceptrons." Water Resources Research, Vol. 56, No. 6, e2019WR026226. 10.1029/2019WR026226

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.

Clark, M.P., Rupp, D.E., Woods, R.A., Zheng, X., Ibbitt, R.P., Slater, A.G., Schmidt, J., and Uddstrom, M.J. (2008). "Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model." Advances in Water Resources, Vol. 31, No. 10, pp. 1309-1324. 10.1016/j.advwatres.2008.06.005

Doucet, A., Godsill, S., and Andrieu, C. (2000). "On sequential Monte Carlo sampling methods for Bayesian filtering." Statistics and Computing, Vol. 10, No. 3, pp. 197-208. 10.1023/A:1008935410038

Evensen, G. (1994). "Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics." Journal of Geophysical Research: Oceans, Vol. 99, No. C5, pp. 10143-10162. 10.1029/94JC00572

Evensen, G. (2003). "The Ensemble Kalman Filter: theoretical formulation and practical implementation." Ocean Dynamics, Vol. 53, No. 4, pp. 343-367. 10.1007/s10236-003-0036-9

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

Hendricks Franssen, H.J., and Kinzelbach, W. (2008). "Real-time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem." Water Resources Research, Vol. 44, No. 9, W09408. doi: 10.1029/2007WR006505. 10.1029/2007WR006505

Kim, Y.S., Lee, G.H., Lee, D.E., and Noh, S.J. (2015). "Parameter estimation and uncertainty assessment of a soil erosion model using data assimilation method." Journal of Korean Society of Hazard Mitigation, Vol. 15, No. 6, pp. 373-382. 10.9798/KOSHAM.2015.15.6.373

Knoben, W.J.M., Freer, J.E., and Woods, R.A. (2019). "Technical note: Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores." Hydrology and Earth System Sciences, Vol. 23, No. 10, pp. 4323-4331. 10.5194/hess-23-4323-2019

Lavenne, A., Thirel, G., Andréassian, V., Perrin, C., and Ramos, M.-H. (2016). "Spatial variability of the parameters of a semi-distributed hydrological model." Proceedings of the International Association of Hydrological Sciences, Vol. 373, pp. 87-94. 10.5194/piahs-373-87-2016

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

Lee, B.J., and Bae, D.-H. (2011). "Development of real-time river flow forecasting model with data assimilation technique." Journal of Korea Water Resources Association, Vol. 44, No. 3, pp. 199-208. 10.3741/JKWRA.2011.44.3.199

Lee, B.J., Jung, I.-W., Jeong, H.-S., and Bae, D.-H. (2013). "Development of realtime dam's hydrologic variables prediction model using observed data assimilation and reservoir operation techniques." Journal of Korea Water Resources Association, Vol. 46, No. 7, pp. 755-765. 10.3741/JKWRA.2013.46.7.755

Lee, D.U., Kim, Y.S., Yu, W.S., and Lee, G.H. (2017). "Evaluation on applicability of on/off-line parameter calibration techniques in rainfall-runoff modeling." Journal of Korea Water Resources Association, Vol. 50, No. 4, pp. 241-252. 10.3741/JKWRA.2017.50.4.241

Liu, Y., Weerts, A.H., Clark, M., Hendricks Franssen, H.-J., Kumar, S., Moradkhani, H., Seo, D.-J., Schwanenberg, D., Smith, P., van Dijk, A.I.J.M., van Velzen, N., He, M., Lee, H., Noh, S.J., Rakovec, O., and Restrepo, P. (2012). "Advancing data assimilation in operational hydrologic forecasting: Progresses, challenges, and emerging opportunities." Hydrology and Earth System Sciences, Vol. 16, No. 10, pp. 3863-3887. 10.5194/hess-16-3863-2012

Le Moine, N. (2008). Le bassin versant de surface vu par le souterrain: Une voie d'amélioration des performances et du réalisme des modèles pluie-débit?. Ph. D. Dissertation, Université Pierre et Marie Curie Paris VI, Paris, France, pp. 149-152.

Noh, S.J. (2013). Sequential Monte Carlo methods for probabilistic forecasts and uncertainty assessment in hydrologic modeling, Ph. D. Dissertation, Kyoto University, Kyoto, Japan.

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, pp. 2707-2721. 10.1016/j.jhydrol.2014.07.049

Noh, S.J., Tachikawa, Y., Shiiba, M., and Kim, S. (2011a). "Dual state-parameter updating scheme on a conceptual hydrologic model using sequential Monte Carlo Filters." Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), Vol. 67, No. 4, p. I_1-I_6. 10.2208/jscejhe.67.I_1

Noh, S.J., Tachikawa, Y., Shiiba, M., and Kim, S. (2011b). "Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization." Hydrology and Earth System Sciences, Vol. 15, No. 10, pp. 3237-3251. 10.5194/hess-15-3237-2011

Noh, S.J., Tachikawa, Y., Shiiba, M., and Kim, S. (2012). "Ensemble Kalman Filtering and particle filtering in a lag-time window for short-term streamflow forecasting with a distributed hydrologic model." Journal of Hydrologic Engineering, Vol. 18, No. 12, pp. 1684-1696. 10.1061/(ASCE)HE.1943-5584.0000751

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

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, Vol. 55, No. 2, pp. 209-222. 10.1080/02626660903546118

Piazzi, G., Thirel, G., Perrin, C., and Delaigue, O. (2021). "Sequential data assimilation for streamflow forecasting: Assessing the sensitivity to uncertainties and updated variables of a conceptual hydrological model at basin scale." Water Resources Research, Vol. 57, No. 4, e2020WR028390. doi: 10.1029/2020WR028390 10.1029/2020WR028390

Ristic, B., Arulampalam, S., and Gordon, N. (2004). Beyond the Kalman Filter: Particle filters for tracking applications. Artech House, Boston, MA, U.S. and London, UK. 10.1155/S1110865704405095

Shen, H., Seo, D.-J., Lee, H., Liu, Y., and Noh, S. (2022). "Improving flood forecasting using conditional bias-aware assimilation of streamflow observations and dynamic assessment of flow-dependent information content." Journal of Hydrology, Vol. 605, 127247. 10.1016/j.jhydrol.2021.127247

Yoo, C., Hwang, J.-H., and Kim, J. (2012). "Use of the extended Kalman Filter for the real-time quality improvement of runoff data: 1. Algorithm construction and application to one station." Journal of Korea Water Resources Association, Vol. 45, No. 7, pp. 697-711. 10.3741/JKWRA.2012.45.7.697

Information

Publisher :KOREA WATER RESOURECES ASSOCIATION
Publisher(Ko) :한국수자원학회
Journal Title :Journal of Korea Water Resources Association
Journal Title(Ko) :한국수자원학회 논문집
Volume : 55
No :10
Pages :761-774
Received Date : 2022-09-02
Revised Date : 2022-09-16
Accepted Date : 2022-09-17
DOI :https://doi.org/10.3741/JKWRA.2022.55.10.761

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

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