Higher Order Markov Chain Model for Synthetic Generation of Daily Streamflows

• Autor(a) principal: PEREIRA,A.G.C.
• Data de Publicação: 2018
• Outros Autores: SOUSA,F.A.S., ANDRADE,B.B., CAMPOS,V.S.M.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512018000300449
• topic: Bayesian Information Criterion; Hydrology; Stochastic Processes

ABSTRACT The aim of this study is to further investigate the two-state Markov chain model for synthetic generation of daily streamflows. The model presented in (4) to determine the state of the stream and later studied in (2) and (3) is based on two Markov chains, both of order one. In some areas of Hydrology, where Markov chains of order one have been successfully used to model events such as daily rainfall, researchers are concerned about the optimal order of the Markov chain (10). In this paper, an answer to a similar concern about the model developed in (4) is given using the Bayesian Information Criterion (BIC) to establish the order of the Markov chain which best fits the data. The methodology is applied to daily flow series from seven Brazilian sites. It is seen that the data generated using the optimal order are closer to the real data than when compared to the model proposed in (4) with the exception of two sites, which exhibit the shortest time series and are located in the driest regions.