Stochastic Generation of Streamflow Time Series
Publication: Journal of Hydrologic Engineering
Volume 23, Issue 10
Abstract
This paper proposes, systematizes, and validates a methodology for stochastically generating streamflow time series at virtually any time scale. Starting from deseasonalized data (i.e., observed series removed of their periodicity), the proposed methodology works by sequentially sampling each new streamflow value from a joint probability density function (PDF) conditioned by the previously generated values. This joint PDF is obtained directly from the observed data, and it represents the probability distribution and probabilistic dependency between consecutive streamflow values. An example application of the series generation methodology was developed based on daily streamflow data from Portugal. The proposed methodology’s results showed good agreement between the observed and generated PDFs. Generated series display less than 10% and 25% deviation, respectively, in terms of the means and standard deviation (for the 0th, 1st, and 2nd order) and the serial autocorrelation, from the observed series. This provides strong evidence for the methodology’s capability for reproducing the streamflow’s autocorrelation structure.
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Acknowledgments
The present work has been developed under a scholarship funded by the Fundação para a Ciência e Tecnologia, of Portugal, with the scholarship reference number PD/BI/113799/2015.
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Information & Authors
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Published In
Journal of Hydrologic Engineering
Volume 23 • Issue 10 • October 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Dec 6, 2017
Accepted: Apr 25, 2018
Published online: Aug 1, 2018
Published in print: Oct 1, 2018
Discussion open until: Jan 1, 2019
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