Combined predictive and descriptive tests for extreme rainfall probability distribution selection

ABSTRACT

The popular approach to select a suitable distribution to characterize extreme rainfall events relies on the assessment of its descriptive performance. This study examines an alternative approach to this task that evaluates, in addition to the descriptive performance of the models, their performance in estimating out-of-sample events (predictive performance). With a numerical experiment and a study case in São Paulo state, Brazil, we evaluated the adequacy of seven probability distributions widely used in hydrological analysis to characterize extreme events in the region and compared the selection process of both popular and altenative frameworks. The results indicate that (1) the popular approach is not capable of selecting distributions with good predictive performance and (2) combining different predictive and descriptive tests can improve the reliability of extreme event prediction. The proposed framework allowed the assessment of model suitability from a regional perspective, identifying the Generalized Extreme Value (GEV) distribution as the most adequate to characterize extreme rainfall events in the region.

KEYWORDS:

• probability distributions
• extreme rainfall events
• statistical modelling
• predictive performance

Editor A. Castellarin Associate Editor T. Kjeldsen

Editor A. Castellarin Associate Editor T. Kjeldsen

Funding

This work was supported by the Brazilian National Council for Scientific and Technological Development – CNPq/Ministry of Science, Technology, and Innovation – MCTI (grant number CNPQ 131238/ 2019-1); the National Council for the Improvement of Higher Education CAPES (finance code 001); and the São Paulo State Research Support Foundation – FAPESP (grant numbers 2015/03806-1 and 2020/08140-0).

Acknowledgements

The authors acknowledge the graduate programme in Hydraulics and Sanitary Engineering – PPGSHS (USP – EESC) – for scientific support.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/02626667.2022.2063725