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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-381
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
12 Jul 2017
Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).
State Updating and Calibration Period Selection to Improve Dynamic Monthly Streamflow Forecasts for a Wetland Management Application
Matthew S. Gibbs1,2, David McInerney1, Greer Humphrey1, Mark A. Thyer1, Holger R. Maier1, Graeme C. Dandy1, and Dmitri Kavetski1 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, PO Box 1047, Adelaide, 5000
Abstract. Sub-seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work has focused on improving forecasts for one such application: the management of water available in an open channel drainage network to maximise environmental and social outcomes in a region in southern Australia. Conceptual rainfall-runoff models with a postprocessor error model for uncertainty analysis were applied to provide forecasts of monthly streamflow. Two aspects were considered to improve the accuracy of the forecasts: 1) state updating to force the models to match observations from the start of the forecast period, and 2) selection of a calibration period representative of the forecast period. Five metrics were used to assess forecast performance, representing the reliability, precision, bias and skill of the forecasts produced, using both observed and forecast climate data. The results indicate that assimilating observed streamflow data into the model, by updating the storage level at the start of a forecast period, improved the performance of the forecasts across the metrics when compared to an approach that “warmed up” the storage levels using historical climate data. The shorter calibration period improved the performance of the forecasts, particularly for a catchment that was expected to have experienced a change in the rainfall-runoff relationship in the past. The results highlight the importance of identifying a calibration record representative of the expected forecast conditions, and if this step is ignored degradation of predictive performance can result.

Citation: Gibbs, M. S., McInerney, D., Humphrey, G., Thyer, M. A., Maier, H. R., Dandy, G. C., and Kavetski, D.: State Updating and Calibration Period Selection to Improve Dynamic Monthly Streamflow Forecasts for a Wetland Management Application, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-381, in review, 2017.
Matthew S. Gibbs et al.
Matthew S. Gibbs et al.
Matthew S. Gibbs et al.

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Short summary
This work developed models to predict how much water will be available next month to maximise environmental and social outcomes for a region in southern Australia. Using observed streamflow data on the day the forecasts was made to initialise the models, instead of only rainfall data, improved the results. Making sure only data representative of the upcoming period to develop the models was also important. If this step was ignored, and more data was used, poor predictions could be produced.
This work developed models to predict how much water will be available next month to maximise...
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