Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
권호 표지
DOI QR코드

DOI QR Code

Multi parameter optimization framework of an event-based rainfall-runoff model with the use of multiple rainfall events based on DDS algorithm

다중 강우사상을 반영한 DDS 알고리즘 기반 단일사상 강우-유출모형 매개변수 최적화 기법

  • Yu, Jae-Ung (Department of Civil and Environmental Engineering, Sejong University) ;
  • Oh, Se-Cheong (Water Resources Facilities Maintenance Department, Korea Water Resources Corporation) ;
  • Lee, Baeg (Rural Research Institute of Korea Rural Community Corporation) ;
  • Kwon, Hyun-Han (Department of Civil and Environmental Engineering, Sejong University)
  • 유재웅 (세종대학교 건설환경공학과) ;
  • 오세청 (수자원공사 수자원환경부문 수자원시설처) ;
  • 이백 (한국농어촌공사 농어촌연구원) ;
  • 권현한 (세종대학교 건설환경공학과)
  • Received : 2022.09.22
  • Accepted : 2022.10.11
  • Published : 2022.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Estimation of the parameters for individual rainfall-rainfall events can lead to a different set of parameters for each event which result in lack of parameter identifiability. Moreover, it becomes even more ambiguous to determine a representative set of parameters for the watershed due to enhanced variability exceeding the range of model parameters. To reduce the variability of estimated parameters, this study proposed a parameter optimization framework with the simultaneous use of multiple rainfall-runoff events based on NSE as an objective function. It was found that the proposed optimization framework could effectively estimate the representative set of parameters pertained to their physical range over the entire watershed. It is found that the difference in NSE value of optimization when it performed individual and multiple rainfall events, is 0.08. Furthermore, In terms of estimating the observed floods, the representative parameters showed a more improved (or similar) performance compared to the results obtained from the single-event optimization process on an NSE basis.

개별 강우-유출 사상을 대상으로 최적 매개변수를 산정하는 경우 사상별로 매개변수가 서로 다르며 물리적인 범위를 중심으로 변동성이 커 유역의 대표 매개변수를 결정하는데 어려움이 있다. 매개변수 추정 시 변동성 증가는 강우의 시공간적 변동성과 함께, 유역 내 일부 홍수량 산정지점 기준으로 강우-유출 자료만이 이용 가능하여 매개변수의 식별성(identifiability)이 매우 낮다. 추정되는 매개변수의 변동성 확대에 따른 문제점을 개선하기 위하여, 본 연구에서는 다수의 사상들을 동시에 고려한 매개변수 최적화 방법을 제안하였으며, NSE를 목적함수로 하여 매개변수를 최적화하였다. 개별 사상들을 통합적으로 고려하여 최적매개변수를 산정하는 경우 매개변수의 물리적인 특성을 유지함과 동시에 유역의 공동 매개변수 효율적으로 추정이 가능하였다. 개별 매개변수와 공통 매개변수 NSE의 차이가 최대 0.08 정도를 나타내며, 홍수량 재현 측면에서도 개별적으로 최적화를 수행한 경우와 유사하거나 보다 개선된 결과를 확인할 수 있었다.

Keywords

Acknowledgement

이 연구는 기상청 「기상·지진See-At기술개발연구」 KMI2018-07010의 지원으로 수행되었습니다.

References

  1. Ahmad, M.M., Ghumman, A.R., and Ahmad, S. (2009). "Estimation of Clark's instantaneous unit hydrograph parameters and development of direct surface runoff hydrograph." Water Resources Management, Vol. 23, No. 12, pp. 2417-2435. https://doi.org/10.1007/s11269-008-9388-8
  2. Ajmal, M., Waseem, M., Ahn, J.-H., and Kim, T.-W. (2015). "Improved runoff estimation using event-based rainfall-runoff models." Water Resources Management, Vol. 29, No. 6, pp. 1995-2010. https://doi.org/10.1007/s11269-015-0924-z
  3. Berthet, L., Andreassian, V., Perrin, C., and Javelle, P. (2009). "How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments." Hydrology and Earth System Sciences, Vol. 13, No. 6, pp. 819-831. https://doi.org/10.5194/hess-13-819-2009
  4. Che, D., Nangare, M., and Mays, L.W. (2014). "Determination of Clark's Unit Hydrograph parameters for watersheds." Journal of Hydrologic Engineering, Vol. 19, No. 2, pp. 384-387. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000796
  5. Chua, L.H., and Wong, T.S. (2010). "Improving event-based rainfall - runoff modeling using a combined artificial neural network - kinematic wave approach." Journal of Hydrology, Vol. 390, No. 1-2, pp. 92-107. https://doi.org/10.1016/j.jhydrol.2010.06.037
  6. Clark, C. (1945). "Storage and the unit hydrograph." Transactions of the American Society of Civil Engineers, Vol. 110, No. 1, pp. 1419-1446. https://doi.org/10.1061/TACEAT.0005800
  7. Duan, Q., Sorooshian, S., and Gupta, V. (1992). "Effective and efficient global optimization for conceptual rainfall-runoff models." Water Resources Research, Vol. 28, No. 4, pp. 1015-1031. https://doi.org/10.1029/91WR02985
  8. Hromadka, T. (1986). San Bernardino county hydrology manual. Irvine, CA, U.S.
  9. Jung, Y.-Y., Yang, S.-K., and Kim, D.-S. (2014). "Flood discharge to decision of parameters in Han Stream watershed." Journal of Environmental Science International, Vol. 23, No. 4, pp. 533-541. https://doi.org/10.5322/JESI.2014.4.533
  10. Kalin, L., Isik, S., Schoonover, J.E., and Lockaby, B.G. (2010). "Predicting water quality in unmonitored watersheds using artificial neural networks." Journal of Environmental Quality, Vol. 39, No. 4, pp. 1429-1440. https://doi.org/10.2134/jeq2009.0441
  11. Kan, G., Yao, C., Li, Q., Li, Z., Yu, Z., Liu, Z., Ding, L., He, X., and Liang, K. (2015). "Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model." Stochastic Environmental Research and Risk Assessment, Vol. 29, No. 5, pp. 1345-1370. https://doi.org/10.1007/s00477-015-1040-6
  12. Kim, J.-Y., Kwon, D.-S., Bae, D.-H., and Kwon, H.-H. (2020). "Bayesian parameter estimation of Clark unit hydrograph using multiple rainfall-runoff data." Journal of Korea Water Resources Association, Vol. 53, No. 5, pp. 383-393. https://doi.org/10.3741/JKWRA.2020.53.5.383
  13. Lee, D.H., Choi, J.I., Shin, S.H., and Yi, J.E. (2013). "A study on proper number of subbasin division for runoff analysis using Clark and ModClark methods in Midsize Basins." Journal of Civil and Environmental Engineering Research, KSCE, Vol. 33, No. 1, pp. 157-170.
  14. Ministry of Environment (ME) (2019). Guideline of design flood estimation,. pp. 25-52.
  15. Ministry of Land, Infrastructure and Trandsport (MLIT) (2021). Basic river plans in Seomjin River, pp. 3-1-3-16.
  16. Nash, J.E., and Sutcliffe, J.V. (1970). "River flow forecasting through conceptual models part I - A discussion of principles." Journal of Hydrology, Vol. 10, No. 3, pp. 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  17. Natarajan, S., and Radhakrishnan, N. (2019). "Simulation of extreme event-based rainfall - runoff process of an urban catchment area using HEC-HMS." Modeling Earth Systems and Environment, Vol. 5, No. 4, pp. 1867-1881. https://doi.org/10.1007/s40808-019-00644-5
  18. Reshma, T., Venkata Reddy, K., Pratap, D., and Agilan, V. (2018). "Parameters optimization using Fuzzy rule based multi-objective genetic algorithm for an event based rainfall-runoff model." Water Resources Management, Vol. 32, No. 4, pp. 1501-1516. https://doi.org/10.1007/s11269-017-1884-2
  19. Sabol, G.V. (1988). "Clark unit hydrograph and R-parameter estimation." Journal of Hydraulic Engineering, Vol. 114, No. 1, pp. 103-111. https://doi.org/10.1061/(ASCE)0733-9429(1988)114:1(103)
  20. Sarkar, A., and Kumar, R. (2012). "Artificial neural networks for event based rainfall-runoff modeling." Journal of Water Resource and Protection, Vol. 4, No. 10, pp. 891-897. https://doi.org/10.4236/jwarp.2012.410105
  21. Tolson, B.A., and Shoemaker, C.A. (2007). "Dynamically dimensioned search algorithm for computationally efficient watershed model calibration." Water Resources Research, Vol. 43, No. 1, pp. 1-16.
  22. Yoon, T.H, and Park, J.W. (2002). "Improvement of the storage coefficient estimating method for the Clark model." Proceedings of the Korea Water Resources Association Conference, KWRA, pp. 1334-1339.