A virtual hydrological framework for evaluation of stochastic rainfall models
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.