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Stochastic Generation of Streamflow Time Series
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.
Stochastic Generation of Streamflow Time Series
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.
Stochastic Generation of Streamflow Time Series
Oliveira, Bruno (author) / Maia, Rodrigo (author)
2018-08-01
Article (Journal)
Electronic Resource
Unknown
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