A multivariate non-parametric model for synthetic generation of daily streamflow

作者：Wang Wensheng^*; Ding Jing

来源：Hydrological Processes, 2007, 21(13): 1764-1771.

DOI：10.1002/hyp.6340

• daily streamflow
• synthetic generation
• kernel density estimation
• multivariate kernel density model
• verification and validation

A p-order multivariate kernel density model based on kernel density theory has been developed for synthetic generation of multivariate variables. It belongs to a kind of data-driven approach and is able to avoid prior assumptions as to the form of probability distribution (normal or Pearson III) and the form of dependence (linear or non-linear). The p-order multivariate kernel density model is a non-parametric method for synthesis of streamflow. The model is more flexible than conventional parametric models used in stochastic hydrology. The effectiveness and satisfactoriness of this model are illustrated through its application to the simultaneous synthetic generation of daily streamflow from Pingshan station and Yibin-Pingshan region (Yi-Ping region) of the Jinsha River in China.

• 出版日期2007-6-30
• 单位四川大学