Bayesian generation of synthetic streamflows: 2. The multivariate case

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Abstract
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).

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