Journal article

Bayesian Approach to Predictor Selection for Seasonal Strearnflow Forecasting

David E Robertson, QJ Wang



Statistical methods commonly used for forecasting climate and streamflows require the selection of appropriate predictors. Poorly designed predictor selection procedures can result in poor forecasts for independent events. This paper introduces a predictor selection method for the Bayesian joint probability modeling approach to seasonal streamflow forecasting at multiple sites. The method compares forecasting models using a pseudo-Bayes factor (PsBF). A stepwise expansion of a base model is carried out by including the candidate predictor with the highest PsBF that exceeds a selection threshold. Predictors representing the initial catchment conditions are selected on their ability to forecas..

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University of Melbourne Researchers


Funding Acknowledgements

This research has been supported by the Water Information Research and Development Alliance between the Australian Bureau of Meteorology and CSIRO Water for a Healthy Country Flagship, the South Eastern Australian Climate Initiative, and the CSIRO OCE Science Leadership Scheme. We thank Neil Plummer, Jeff Perkins, Dr. Senlin Zhou, Andrew Schepen, Trudy Peatey, and Dr. Daehyok Shin from the Australian Bureau of Meteorology for many valuable discussions as well as providing the streamflow and rainfall data for this study. Tom Pagano and Prasantha Hapuarachchi have contributed to the quality of the publication through their review of an early version of this manuscript.