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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2018-489
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-489
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Sep 2018

Research article | 25 Sep 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

A virtual hydrological framework for evaluation of stochastic rainfall models

Bree Bennett1, Mark Thyer1, Michael Leonard1, Martin Lambert1, and Bryson Bates2 Bree Bennett et al.
  • 1School of Civil, Environmental and Mining Engineering, University of Adelaide, North Terrace Campus, 5005, South Australia
  • 2School of Agriculture and Environment, The University of Western Australia, Crawley, 6009, Western Australia

Abstract. Stochastic rainfall modelling is a commonly used technique for evaluating the impact of flooding, drought or climate change in a catchment. While considerable attention is given to the development of stochastic rainfall models, significantly less attention is given to performance evaluation methods. Typical evaluation methods employ a variety of rainfall statistics. However, they give limited understanding about which rainfall characteristics are most important for reliable streamflow prediction whenever the simulated rainfall are poor. To address this issue a new evaluation method for rainfall models is introduced, with three key features: (i) streamflow-based – to give a direct evaluation of modelled streamflow performance, (ii) virtual – to avoid the issue of confounding errors in hydrological models or data, and (iii) targeted – to isolate the source of errors according to specific sites and months. The virtual hydrologic evaluation framework is applied to a case study of 22 sites in South Australia. The framework demonstrated that apparently good modelled rainfall can produce poor streamflow predictions, whilst poor modelled rainfall may lead to good streamflow predictions, as catchment processes can dampen or amplify rainfall errors when converted to streamflow. The framework identified the importance of rainfall in the wetting-up months of the catchment cycle (May and June in this case study) for providing reliable predictions of streamflow over the entire year despite their low monthly flow volume. This insight would not have been found using existing methods and highlights the importance of the virtual hydrological evaluation framework for stochastic rainfall model evaluation.

Bree Bennett et al.
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Bree Bennett et al.
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Short summary
A new stochastic rainfall model evaluation framework is introduced, with three key features: (1) streamflow-based – to directly evaluate modelled streamflow performance, (2) virtual – to avoid confounding errors in hydrological models or data, and (3) targeted – to isolate errors according to specific sites/months. The framework identified the importance of rainfall in the wetting-up months for providing reliable predictions of streamflow over the entire year despite their low flow volumes.
A new stochastic rainfall model evaluation framework is introduced, with three key features: (1)...
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