Journal article
A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites
QJ Wang, DE Robertson, FHS Chiew
Water Resources Research | AMER GEOPHYSICAL UNION | Published : 2009
DOI: 10.1029/2008WR007355
Abstract
Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Nino-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric struc..
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Funding Acknowledgements
This research has been supported by the CSIRO CEO Science Leadership Scheme, the South Eastern Australian Climate Initiative, and the Water Information Research and Development Alliance between the Australian Bureau of Meteorology and CSIRO Water for a Healthy Country Flagship. Andrew Frost and Peter Hairsine made valuable suggestions on an early draft of the paper. Constructive comments from three anonymous reviewers led to substantial strengthening of the paper, in particular on forecast verification. Discussions with a number of colleagues from the Australian Bureau of Meteorology, CSIRO, and other organizations helped clarify a number of ideas.