Optimization of flood forecasting model parameters and analysis of influencing factors
In this paper,26 floods from 1980 to 2016 in the controlled basin of Gaoguan Reservoir are selected,and the sensitivity parameters of the Xin'an River flood forecasting model are screened by using multi-parameter sensitivity analysis(MPSA).The uncertainty of sensitivity parameters is analyzed by using generalized likelihood uncertainty estimation(GLUE)method,and the posterior distribution ranges of some of the sensitivity parameters are determined.The flood data are divided into two periods from 1980 to 1997 and from 1998 to 2016,and genetic algorithm(GA)and adaptive particle swarm optimization(APSO)algorithm are used to calibrate and verify the screened sensitive parameters,and the sensitive parameters and regional land use changes in different periods are analyzed.The results show that:1)among the sensitive parameters of Xin'an River model adopted in the study basin,the uncertainty ranges of impervious surface coverage(IMP),groundwater daily runoff coefficient(KG),soil flow daily runoff coefficient(KSS),soil flow recession coefficient(KKSS),dynamic equation water storage index(n),and dynamic equation water storage coefficient(c)are small,and the value ranges are more concentrated than the previous empirical ranges;2)both GA and APSO have good adaptability in the Xin'an River model parameter calibration in the study area,but the comprehensive performance of APSO is better than that of GA;3)when comparing the two time periods,IMP and n significantly increased,the surface free water storage capacity(SM)significantly decreased,and the conditions of flow production and confluence in the study area changed significantly.
Xin'an River modelparameter sensitivity analysisparameter uncertainty analysisparameter calibrationanalysis of influencing factors
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