Journal article
The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia
P Pokhrel, QJ Wang, DE Robertson
Water Resources Research | AMER GEOPHYSICAL UNION | Published : 2013
DOI: 10.1002/wrcr.20449
Abstract
Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with differ..
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Funding Acknowledgements
This research has been funded by the South Eastern Australian Climate Initiative (SEACI). Partial support for the research is also provided by the Water Information Research and Development Alliance (WIRADA) between CSIRO's Water for a Healthy Country Flagship and the Bureau of Meteorology, and by the CSIRO OCE Science Leadership Scheme. Data used in this study were provided by Melbourne Water, Hydro Tasmania, the Murray Darling Basin Authority, Goulburn-Murray Water, the Queensland Department of Environment and Resource Management, the Bureau of Meteorology's Climate and Water Division, and the Centre for Australian Weather and Climate Research. We would like to acknowledge James C. Bennett for his help in editing the manuscript and Andrew Schepen for making the POAMA Climate index forecasts available to us. We would also like to thank the Associated Editor and three anonymous reviewers, whose comments and suggestions help improve the paper substantially.