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...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.展开更多
This paper investigates the maximum energy efficiency level and the energy saving potentials in each region in China that can be practically attained at current economic and technological development levels. Most of t...This paper investigates the maximum energy efficiency level and the energy saving potentials in each region in China that can be practically attained at current economic and technological development levels. Most of the nation ＇s energy efficient provinces are found along the coast of southeast China, while most of its least energy efficient provinces are in the hinterland that is rich in coal resources, and which depends heavily on coal consumption. China＇s low efficiency in energy resource allocation stems from its secondary industry, which is handicapped by the lowest energy efficiency and the most striking regional differentials. A comparison of the factors affecting the energy efficiency shows that the provinces being compared in this study differ tremendously in energy consumption structure, technological level of the secondary industry, and abundance of energy resources, and that the other factors are only adequate, rather than necessary, conditions. It is imperative to rectify the behaviors of provinces in balancing local energy allocation, to channel energy resources to energy efficient provinces, and to improve the national energy efficiency as a whole. When taking energy-saving steps, provinces must take into full consideration both the national and local factors that affect energy efficiency. Furthermore, it is unrealistic for China to set a unified energy saving goal for differentprovinces.展开更多
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)
文摘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.
基金a periodic result of the State Natural Science Foundation project "Regional Differences in Energy Efficiency in China and the Impacting Factors" chaired by the author.
文摘This paper investigates the maximum energy efficiency level and the energy saving potentials in each region in China that can be practically attained at current economic and technological development levels. Most of the nation ＇s energy efficient provinces are found along the coast of southeast China, while most of its least energy efficient provinces are in the hinterland that is rich in coal resources, and which depends heavily on coal consumption. China＇s low efficiency in energy resource allocation stems from its secondary industry, which is handicapped by the lowest energy efficiency and the most striking regional differentials. A comparison of the factors affecting the energy efficiency shows that the provinces being compared in this study differ tremendously in energy consumption structure, technological level of the secondary industry, and abundance of energy resources, and that the other factors are only adequate, rather than necessary, conditions. It is imperative to rectify the behaviors of provinces in balancing local energy allocation, to channel energy resources to energy efficient provinces, and to improve the national energy efficiency as a whole. When taking energy-saving steps, provinces must take into full consideration both the national and local factors that affect energy efficiency. Furthermore, it is unrealistic for China to set a unified energy saving goal for differentprovinces.
