A Bayesian method for multi-site stochastic data generation: Dealing with non-concurrent and missing data, variable transformation and parameter uncertainty

Abstract

Stochastically generated stream flow and climatic data may be used as input to water resources simulation models for planning purposes. Dealing with non-concurrent and missing data, variable transformation and parameter uncertainty presents a significant challenge in the development of methods for stochastic data generation. In this paper, a Bayesian method is introduced for multi-site stochastic generation of annual stream flow and climatic data. A contemporaneous autoregressive lag-one model CAR(1) with the Box-Cox transformation is used to capture key statistical structure of multiple annual stream flow and climatic time series while keeping the number of model parameters to a minimum. Th..