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Bayesian generation of synthetic streamflows: 2. The multivariate case
Author(s) -
Valdés Juan B.,
RodríguezIturbe Ignacio,
Vicens Guillermo J.
Publication year - 1977
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr013i002p00291
Subject(s) - multivariate statistics , autoregressive model , bayesian probability , computer science , extension (predicate logic) , synthetic data , process (computing) , data mining , statistics , econometrics , artificial intelligence , mathematics , machine learning , programming language , operating system
A Bayesian framework for the synthetic generation of annual Streamflows from a multivariate first‐ order autoregressive model is presented. This framework allows the user to include directly the effects of the parameter uncertainties in the evaluation of proposed projects through simulation with the synthetically generated records. The model produces synthetic traces with higher variances than those in the historical records when the records are too short to estimate reliably the ‘time’ values of the parameters of the process. This multivariate model is a natural extension of the model presented by Vicens et al. (1975).