A virtual hydrological framework for evaluation of stochastic rainfall models
<p>Stochastic rainfall modelling is a commonly used technique for evaluating the impact of flooding, drought, or climate change in a catchment. While considerable attention has been given to the development of stochastic rainfall models (SRMs), significantly less attention has been paid to dev...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-11-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/23/4783/2019/hess-23-4783-2019.pdf |
| Summary: | <p>Stochastic rainfall modelling is a commonly used
technique for evaluating the impact of flooding, drought, or climate change
in a catchment. While considerable attention has been given to the
development of stochastic rainfall models (SRMs), significantly less
attention has been paid to developing methods to evaluate their performance.
Typical evaluation methods employ a wide range of rainfall statistics.
However, they give limited understanding about which rainfall statistical
characteristics are most important for reliable streamflow prediction. To
address this issue a formal evaluation framework 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 seasons. The
virtual hydrological evaluation framework uses two types of tests,
integrated tests and unit tests, to attribute deficiencies that impact on
streamflow to their original source in the SRM according to site and season.
The framework is applied to a case study of 22 sites in South Australia with
a strong seasonal cycle. In this case study, the framework demonstrated the
surprising result that apparently “good” modelled rainfall can produce
“poor” streamflow predictions, whilst “poor” modelled rainfall may lead to
“good” streamflow predictions. This is due to the representation of highly
seasonal catchment processes within the hydrological model that can dampen
or amplify rainfall errors when converted to streamflow. The framework
identified the importance of rainfall in the “wetting-up” months (months
where the rainfall is high but streamflow low) of the annual hydrologic
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 SRM
evaluation.</p> |
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| ISSN: | 1027-5606 1607-7938 |