Efficiency and Feasibility of an Integrated Algorithm for Distributed Hydrological M odel Calibration  

作　　者：王宇晖 牛瑞华 韩耀宗 雷晓辉 蒋云钟 宋新山 

机构地区：[1]College of Environmental Science and Engineering,Donghua University [2]State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research

出　　处：《Journal of Donghua University(English Edition)》2013年第4期323-329,共7页东华大学学报（英文版）

基　　金：National Basic Research Program(973)of China(No.2010CB951102);Innovative Research Groups of the National Natural Science Foundation,China(No.51021006);National Natural Science Foundation of China(No.51079028)

摘　　要：Increasing complexity of distributed hydrological model （DHM） has lowered the efficiency of convergence.In this study,global sensitivity analysis （SA） was introduced by combining multiobjective （MO） optimization for DHM calibration.Latin Hypercube-once at a time （LH-OAT） was adopted in global parameter SA to obtain relative sensitivity of model parameter,which can be categorized into different sensitivity levels.Two comparative study cases were conducted to present the efficiency and feasibility by combining SA with MO（SA-MO）.WetSpa model with non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ） algorithm and EasyDHM model with multi-objective sequential complex evolutionary metropolis-uncertainty analysis （MOSCEM-UA）algorithm were adopted to demonstrate the general feasibility of combining SA in optimization.Results showed that the LH-OAT was globally effective in selecting high sensitivity parameters.It proves that using parameter from high sensitivity groups results in higher convergence efficiency.Study case Ⅰ showed a better Pareto front distribution and convergence compared with model calibration without SA.Study case Ⅱ indicated a more efficient convergence of parameters in sequential evolution of MOSCEM-UA under the same iteration.It indicates that SA-MO is feasible and efficient for high dimensional DHM calibration.Increasing complexity of distributed hydrological model(DHM)has lowered the efficiency of convergence.In this study,global sensitivity analysis(SA)was introduced by combining multiobjective(MO)optimization for DHM calibration.Latin Hypercube-once at a time(LH-OAT)was adopted in global parameter SA to obtain relative sensitivity of model parameter,which can be categorized into different sensitivity levels.Two comparative study cases were conducted to present the efficiency and feasibility by combining SA with MO(SA-MO).WetSpa model with non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)algorithm and EasyDHM model with multi-objective sequential complex evolutionary metropolis-uncertainty analysis(MOSCEM-UA)algorithm were adopted to demonstrate the general feasibility of combining SA in optimization.Results showed that the LH-OAT was globally effective in selecting high sensitivity parameters.It proves that using parameter from high sensitivity groups results in higher convergence efficiency.Study case I showed a better Pareto front distribution and convergence compared with model calibration without SA.Study case II indicated a more efficient convergence of parameters in sequential evolution of MOSCEM-UA under the same iteration.It indicates that SA-MO is feasible and efficient for high dimensional DHM calibration.

关 键 词：distributed hydrological model （DHM） optimization sensitivity analysis multi-objective （MO） convergence efficiency calibrationCLC number：TV211.1＋1Document code：AArticle ID：1672-5220（2013）04-0323-07 

分 类 号：TV211.11[水利工程—水文学及水资源]