A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts
M Li, QJ Wang, JC Bennett, DE Robertson
HYDROLOGY AND EARTH SYSTEM SCIENCES | COPERNICUS GESELLSCHAFT MBH | Published : 2015
For streamflow forecasting, rainfall-runoff models are often augmented with updating procedures that correct forecasts based on the latest available streamflow observations of streamflow. A popular approach for updating forecasts is autoregressive (AR) models, which exploit the "memory" in hydrological model simulation errors. AR models may be applied to raw errors directly or to normalised errors. In this study, we demonstrate that AR models applied in either way can sometimes cause over-correction of forecasts. In using an AR model applied to raw errors, the over-correction usually occurs when streamflow is rapidly receding. In applying an AR model to normalised errors, the over-correction..View full abstract
This work is part of the WIRADA (Water Information Research and Development Alliance) streamflow forecasting project funded under CSIRO Water for a Healthy Country Flagship. We would like to thank Durga Shrestha (CSIRO) for valuable suggestions that led to substantial strengthening of the manuscript. We would like to thank two reviewers, Bettina Schaefli and Mark Thyer, for their careful reviews and valuable recommendations, which have improved the quality of this manuscript considerably.