[Objective]The study aimed to simulate the production and transportation process of surface runoff,sediment and non-point source pollution in Xincai River basin based on SWAT model.[Method]On the basis of analyzing th...[Objective]The study aimed to simulate the production and transportation process of surface runoff,sediment and non-point source pollution in Xincai River basin based on SWAT model.[Method]On the basis of analyzing the principles of SWAT model,the correlative parameters of runoff,sediment and water quality were calibrated,then the spatial and temporal distribution of runoff,sediment and non-point source pollutants in Xincai River basin were studied by using SWAT model.[Result]The results of calibration and validation showed that SWAT model was reasonable and available,and it can be used to simulate the non-point source pollution of Xincai River basin.The simulation results revealed that the load of sediment and various pollutants was the highest in the rainy year,followed by the normal year,while it was the minimum in the dry year,indicating that the production of sediment and non-point source pollutants was closely related to annual runoff.[Conclusion]The research could provide scientific references for the prevention of non-point source pollution in a basin.展开更多
This work (in two parts) will present a novel predictive modeling methodology aimed at obtaining “best-estimate results with reduced uncertainties” for the first four moments (mean values, covariance, skewness and k...This work (in two parts) will present a novel predictive modeling methodology aimed at obtaining “best-estimate results with reduced uncertainties” for the first four moments (mean values, covariance, skewness and kurtosis) of the optimally predicted distribution of model results and calibrated model parameters, by combining fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters. Underlying the construction of this fourth-order predictive modeling methodology is the “maximum entropy principle” which is initially used to obtain a novel closed-form expression of the (moments-constrained) fourth-order Maximum Entropy (MaxEnt) probability distribution constructed from the first four moments (means, covariances, skewness, kurtosis), which are assumed to be known, of an otherwise unknown distribution of a high-dimensional multivariate uncertain quantity of interest. This fourth-order MaxEnt distribution provides optimal compatibility of the available information while simultaneously ensuring minimal spurious information content, yielding an estimate of a probability density with the highest uncertainty among all densities satisfying the known moment constraints. Since this novel generic fourth-order MaxEnt distribution is of interest in its own right for applications in addition to predictive modeling, its construction is presented separately, in this first part of a two-part work. The fourth-order predictive modeling methodology that will be constructed by particularizing this generic fourth-order MaxEnt distribution will be presented in the accompanying work (Part-2).展开更多
文摘[Objective]The study aimed to simulate the production and transportation process of surface runoff,sediment and non-point source pollution in Xincai River basin based on SWAT model.[Method]On the basis of analyzing the principles of SWAT model,the correlative parameters of runoff,sediment and water quality were calibrated,then the spatial and temporal distribution of runoff,sediment and non-point source pollutants in Xincai River basin were studied by using SWAT model.[Result]The results of calibration and validation showed that SWAT model was reasonable and available,and it can be used to simulate the non-point source pollution of Xincai River basin.The simulation results revealed that the load of sediment and various pollutants was the highest in the rainy year,followed by the normal year,while it was the minimum in the dry year,indicating that the production of sediment and non-point source pollutants was closely related to annual runoff.[Conclusion]The research could provide scientific references for the prevention of non-point source pollution in a basin.
文摘This work (in two parts) will present a novel predictive modeling methodology aimed at obtaining “best-estimate results with reduced uncertainties” for the first four moments (mean values, covariance, skewness and kurtosis) of the optimally predicted distribution of model results and calibrated model parameters, by combining fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters. Underlying the construction of this fourth-order predictive modeling methodology is the “maximum entropy principle” which is initially used to obtain a novel closed-form expression of the (moments-constrained) fourth-order Maximum Entropy (MaxEnt) probability distribution constructed from the first four moments (means, covariances, skewness, kurtosis), which are assumed to be known, of an otherwise unknown distribution of a high-dimensional multivariate uncertain quantity of interest. This fourth-order MaxEnt distribution provides optimal compatibility of the available information while simultaneously ensuring minimal spurious information content, yielding an estimate of a probability density with the highest uncertainty among all densities satisfying the known moment constraints. Since this novel generic fourth-order MaxEnt distribution is of interest in its own right for applications in addition to predictive modeling, its construction is presented separately, in this first part of a two-part work. The fourth-order predictive modeling methodology that will be constructed by particularizing this generic fourth-order MaxEnt distribution will be presented in the accompanying work (Part-2).
