A new multi-objective optimization scheme based on surrogate modeling was proposed.Taking the Dapoling catchment as a case study, the response relationship between the parameter of Xin’anjiang model and different objectives was constructed based on multivariate adaptive regression splines,to estimate the Pareto sets or non-dominant solutions.Four objective functions of overall water balance error,root mean square error,relative error of peak flows,and low flows were used to optimize model parameters,and four evaluation criteria of Nash-Sutcliffe efficiency coefficient (NSE),relative error of peak flow and runoff volume (REPF and RERV),and time error of peak flow (TEPF) were selected to quantify the goodness-of-fit of observations against simulation model calculated values.In addition,four uncertainty criteria were applied to assess the hydrological uncertainty ranges with the Pareto solutions for ten flood events.Results demonstrated that the surrogate-modeling based method increases the feasibility of applying parameter optimization to computationally intensive simulation models via reducing the number of simulation runs.Simultaneously,uncertainty analysis results also revealed that the proposed method based on surrogate modeling is high efficiency and easy to operate.Thereby,the method is feasible for practical operations for complex simulation models in model calibration and uncertainty analysis.
