Synthetic generation of hydrologic time series based on nonparametric random generation

Tae Woong Kim, Juan B Valdes

Research output: Contribution to journalArticle

19 Scopus citations


Synthetic hydrologic time series can be used to quantify the uncertainty of a water resources system. Conventional parametric models, such as autoregressive moving average or Markovian models, assume that the variable under consideration is Gaussian. This assumption, however, is a shortcoming of parametric models and motivates the development of nonparametric approaches. Nonparametric models based on a kernel function have an innate low-order structure and are restricted to highly persistent variables. This study presented a seminonparametric (SNP) model that takes advantage of both parametric and nonparametric models to generate monthly precipitation and temperature in the Conchos River Basin in Mexico. By adopting a consistent and robust scheme from the Markovian model and a nonparametric mechanism to generate a distribution-free random component, the SNP model reliably reproduced sample properties such as mean, variance, correlation, and multimodality in the probability density function. Journal of Hydrologic Engineering

Original languageEnglish (US)
Pages (from-to)395-404
Number of pages10
JournalJournal of Hydrologic Engineering
Issue number5
Publication statusPublished - Sep 2005



  • Hydrologic models
  • Precipitation
  • Random variables
  • Temperature
  • Time series analysis

ASJC Scopus subject areas

  • Environmental Science(all)
  • Environmental Chemistry
  • Water Science and Technology
  • Civil and Structural Engineering

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