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Volume 22, issue 1 | Copyright

Special issue: Sub-seasonal to seasonal hydrological forecasting

Hydrol. Earth Syst. Sci., 22, 871-887, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 01 Feb 2018

Research article | 01 Feb 2018

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

Matthew S. Gibbs1,2, David McInerney1, Greer Humphrey1, Mark A. Thyer1, Holger R. Maier1, Graeme C. Dandy1, and Dmitri Kavetski1 Matthew S. Gibbs et al.
  • 1School of Civil, Environmental and Mining Engineering, The University of Adelaide, North Terrace, Adelaide, South Australia, 5005, Australia
  • 2Department of Environment, Water and Natural Resources, Government of South Australia, P.O. Box 1047, Adelaide, 5000, Australia

Abstract. Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall–runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.

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This work developed models to predict how much water will be available in the next month to maximise environmental and social outcomes in southern Australia. Initialising the models with observed streamflow data, instead of warmed up by rainfall data, improved the results, even at a monthly lead time, making sure only data representative of the forecast period to develop the models were also important. If this step was ignored, and instead all data were used, poor predictions could be produced.
This work developed models to predict how much water will be available in the next month to...