Journal article
A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions
P Pokhrel, DE Robertson, QJ Wang
Hydrology and Earth System Sciences | COPERNICUS GESELLSCHAFT MBH | Published : 2013
Abstract
Hydrologic model predictions are often biased and subject to heteroscedastic errors originating from various sources including data, model structure and parameter calibration. Statistical post-processors are applied to reduce such errors and quantify uncertainty in the predictions. In this study, we investigate the use of a statistical post-processor based on the Bayesian joint probability (BJP) modelling approach to reduce errors and quantify uncertainty in streamflow predictions generated from a monthly water balance model. The BJP post-processor reduces errors through elimination of systematic bias and through transient errors updating. It uses a parametric transformation to normalize dat..
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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. Streamflow and GIS data were provided by the Murray-Darling Basin Authority, Melbourne Water, HydroTasmania, Goulburn-Murray Water, the Australian Bureau of Meteorology and the Queensland Department of Environment and Resource Management. We would like to acknowledge James C. Bennett and Roger Hughes for their useful comments and suggestions as well as their help in editing the manuscript. We would also like to thank the two anonymous reviewers for their insightful comments and suggestions, which have helped to improve the paper substantially.