Precipitation Simulation Based on k-Nearest Neighbor Approach Using Gamma Kernel
Publication: Journal of Hydrologic Engineering
Volume 18, Issue 5
Abstract
This paper presents a weather generator that produces new values of precipitation to generate realistic weather sequences. The model has been applied to a network of 14 meteorological stations around the Upper Thames River Basin (UTRB), Ontario, Canada. We developed a simple model that employs the k-nearest neighbor resampling approach with gamma kernel perturbation. This gamma kernel perturbation enables the production of new values rather than merely reshuffling the historical data to generate realistic weather sequences. Daily precipitation was simulated at all the locations in and around the considered basin. The comparison of simulated data to the observed data led to the conclusion that the proposed perturbation algorithm performs quite well at preserving the monthly and annual historical statistics. The improved model was shown to produce precipitation amounts different from those observed in the past record.
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Acknowledgments
The research presented in this paper was funded by a Canadian Commonwealth Scholarship program, awarded to the first author from the Canadian Bureau for International Education to pursue research at University of Waterloo, Waterloo, ON, Canada. The writers are particularly grateful for useful suggestions and helpful comments from three anonymous reviewers.
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Information & Authors
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Published In
Journal of Hydrologic Engineering
Volume 18 • Issue 5 • May 2013
Pages: 481 - 487
Copyright
© 2013 American Society of Civil Engineers.
History
Received: May 3, 2011
Accepted: Mar 15, 2012
Published online: Mar 19, 2012
Published in print: May 1, 2013
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