Application of modified SCEM-UA algorithm for parameter optimization of conceptual rainfall-runoff model
CAO Fei-feng1, YAN Qi-bin1, ZHANG Shi-qiang2
1. Bureau of Water Resources and Hydropower Engineering of Zhejiang Province, Hangzhou 310020, China; 2. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
Abstract：Covariance of proposal distribution and the acceptance rate of SCEM-UA algorithm are modified based on the principle of adaptive Metropolis. The modified SCEM-UA algorithm can effectively ensure the diversity of the population and capability of the global search capability as well as computational efficiency, avoiding the premature convergence. The efficiency and effectiveness of modified SCEM-UA algorithm for sampling the posterior distribution of model parameters is discussed based on the case study of the Min River Basin. The results show that modified SCEM-UA algorithm is consistent, effective and efficient in inferring the parameter posterior distribution, and is much better than the original algorithm in the aspects of computation efficiency and accuracy.
曹飞凤, 严齐斌, 张世强. 改进SCEM-UA算法在概念性降雨-径流模型参数优选中的应用[J]. 系统工程理论与实践, 2012, (6): 1362-1368.
CAO Fei-feng, YAN Qi-bin, ZHANG Shi-qiang. Application of modified SCEM-UA algorithm for parameter optimization of conceptual rainfall-runoff model. Systems Engineering - Theory & Practice, 2012, (6): 1362-1368.
[1] Beven K J, Freer J. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems[J]. Journal of Hydrology, 2001(249): 11-29. [2] Beven K J. Calibration, validation and equifinality in hydrological modeling[J]. Anderson M G, Bates P D. Validation in Hydrological Modeling, John Wiley and Sons, Chichester, UK, 2001a: 43-55. [3] Kuczera G, Parent E. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm[J]. Journal of Hydrology, 1998, 211(1/4): 69-85. [4] Blasone R S, Madsen H, Rosbjerg D. Uncertainty assessment of integrated distributed hydrological models using GLUE with Markov chain Monte Carlo sampling[J]. Journal of Hydrology, 2008(353): 18-32. [5] Blasone R S, Vrugt J A, Madsen H, et al. Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling[J]. Advances in Water Resources, 2008(31): 630-648. [6] 卫晓婧, 熊立华, 万民, 等. 融合Markov链-蒙特卡洛算法的改进通用似然不确定性估计估计方法在流域水文模型中的应用[J]. 水利学报, 2009, 40(4): 464-480. Wei X J, Xiong L H, Wan M, et al. Application of Markov chain Monte Carlo method based modified generalized likelihood uncertainty estimation to hydrologic models[J]. Shuili Xuebao, 2009, 40(4): 464-480. [7] Vrugt J A, Gupta H V, Bouten W, et al. A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters[J]. Water Resources Research, 2003, 39(8): 1201, doi: 10.1029/2002WR001642. [8] 曹飞凤, 许月萍, 玄英姬, 等.流域决策的不确定性分析[J].浙江大学学报:工学版, 2009, 43(1): 188-192. Cao F F, Xu Y P, Xuan Y J, et al. Uncertainty analysis for decision-making in river basin management[J]. Journal of Zhejiang University: Engineering Science, 2009, 43(1): 188-192. [9] Thiemann M, Trosset M, Gupta H, et al. Bayesian recursive parameter estimation for hydrological models[J]. Water Resources Research, 2001, 37(10): 2521-2535. [10] Haario H, Saksman E, Tamminen J. An adaptive metropolis algorithm[J]. Bernoulli, 2001, 7(2): 223-242. [11] Gelman A G, Roberts G O, Gilks W R. Efficient Metropolis jumping rules[C]// Bernardo J M, Berger J O, David A F, et al. Bayesian Statistics V, 599-608. Oxford: Oxford University Press, 1996. [12] Gelman A, Rubin D B. Inference from iterative simulation using multiple sequences[J]. Statistic Science, 1992(7): 457-472. [13] Ye W, Bates B C, Viney N R, et al. Performance of conceptual rainfall-runoff models in low-yielding ephemeral catchments[J]. Water Resources Research, 1997, 33(1): 153-166. [14] Jakeman A J, Hornberger G M. How much complexity is warranted in a rainfall-runoff model?[J]. Water Resources Research, 1993, 29(8): 2637-2649.
2012, Vol. 
