Journal article
Improving statistical forecasts of seasonal streamflows using hydrological model output
DE Robertson, P Pokhrel, QJ Wang
Hydrology and Earth System Sciences | COPERNICUS GESELLSCHAFT MBH | Published : 2013
Open access
Abstract
Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid foreca..
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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. We would like to thank Jeff Perkins, Senlin Zhou, Andrew Schepen, Trudy Wilson and Daehyok Shin from the Australian Bureau of Meteorology for many valuable discussions, as well as providing the rainfall and climate index data for this study. 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.