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

✍ Scribed by Wensheng Wang; Jing Ding

Publisher
John Wiley and Sons
Year
2007
Tongue
English
Weight
208 KB
Volume
21
Category
Article
ISSN
0885-6087

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✦ Synopsis

Abstract

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. Copyright © 2007 John Wiley & Sons, Ltd.

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