Synthetic Generation of Hydrologic Time Series Based on Nonparametric Random Generation
Publication: Journal of Hydrologic Engineering
Volume 10, Issue 5
Abstract
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.
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Acknowledgments
This study is based on work supported by SAHRA (Sustainability of semi-Arid Hydrology and Riparian Areas) at the University of Ariz. under the STC Program of the National Science Foundation, Agreement No. EAR-9876800. We are grateful to Drs. Bart Nijssen and Don Davis (at the University of Arizona, Tucson, Arizona), Dr. Chulsang Yoo (at Korea University, Seoul, Korea), and anonymous reviewers for valuable comments.
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Published In
Journal of Hydrologic Engineering
Volume 10 • Issue 5 • September 2005
Pages: 395 - 404
Copyright
© 2005 ASCE.
History
Received: Apr 14, 2004
Accepted: Dec 20, 2004
Published online: Sep 1, 2005
Published in print: Sep 2005
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