Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/109269
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Type: Journal article
Title: State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application
Author: Gibbs, M.
McInerney, D.
Humphrey, G.
Thyer, M.
Maier, H.
Dandy, G.
Kavetski, D.
Citation: Hydrology and Earth System Sciences Discussions, 2018; 22(1):871-887
Publisher: Copernicus Publications on behalf of the European Geosciences Union
Issue Date: 2018
ISSN: 1812-2108
1812-2116
Statement of
Responsibility: 
Matthew S. Gibbs, David McInerney, Greer Humphrey, Mark A. Thyer, Holger R. Maier, Graeme C. Dandy and Dmitri Kavetski
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.
Rights: © Author(s) 2017. This work is distributed under the Creative Commons Attribution 4.0 License.
RMID: 0030076635
DOI: 10.5194/hess-22-871-2018
Grant ID: http://purl.org/au-research/grants/arc/LP140100978
Appears in Collections:Civil and Environmental Engineering publications

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