Higher order Markov Chain Model for Synthetic Generation of Daily Streamflows

A. G. C. Pereira, F. A. S. Sousa, B. B. Andrade, Viviane Simioli Medeiros Campos

Abstract


The aim of this study is to get further into the two-state Markov chain model for synthetic generation daily streamflows. The model proposed in Aksoy and Bayazit (2000) and Aksoy (2003) is based on a two Markov chains for determining the state of the stream. The ascension curve of the hydrograph is modeled by a two-parameter Gamma probability distribution function and is assumed that a recession curve of the hydrograph follows an exponentially function. In this work, instead of assuming a pre-defined order for the Markov chains involved in the modelling of streamflows, a BIC test is performed to establish the Markov chain order that best fit on the data. The methodology was applied to data from seven Brazilian sites. The model proposed here was  better than that one proposed by Aksoy but for two sites which have the lowest time series and are located in the driest regions.

Keywords


Two state Markov chain; higher order Markov chains; gamma distribution; daily streamflows; ascension and recession curves

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References


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DOI: https://doi.org/10.5540/tema.2018.019.03.449

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Trends in Computational and Applied Mathematics

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