Articles | Volume 23, issue 11
https://doi.org/10.5194/hess-23-4783-2019
https://doi.org/10.5194/hess-23-4783-2019
Research article
 | 
25 Nov 2019
Research article |  | 25 Nov 2019

A virtual hydrological framework for evaluation of stochastic rainfall models

Bree Bennett, Mark Thyer, Michael Leonard, Martin Lambert, and Bryson Bates

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Latest update: 14 Dec 2024
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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.