Articles | Volume 27, issue 20
Research article
20 Oct 2023
Research article |  | 20 Oct 2023

A principal-component-based strategy for regionalisation of precipitation intensity–duration–frequency (IDF) statistics

Kajsa Maria Parding, Rasmus Emil Benestad, Anita Verpe Dyrrdal, and Julia Lutz

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Cited articles

Ali, A., Clarke, G. M., and Trustrum, K.: Principal component analysis applied to some data from fruit nutrition experiments, Statistician, 34, 365–369, 1985. a
Barbieri, M. M. and Berger, J. O.: Optimal predictive model selection, Ann. Stat., 32, 870–897, 2004. a
Benestad, R. E.: Implications of a decrease in the precipitation area for the past and the future, Environ. Res. Lett., 13, 044022,, 2018. a
Benestad, R. E., Mezghani, A., and Parding, K. M.: Esd V1.0, Zenodo [code],, 2015. a
Benestad, R. E., Parding, K., Mezghani, A., Dobler, A., Landgren, O., Erlandsen, H., Lutz, J., and Haugen, J.: Stress Testing for Climate Impacts with “Synthetic Storms”, Eos, 100,, 2019a. a
Short summary
Intensity–duration–frequency (IDF) curves describe the likelihood of extreme rainfall and are used in hydrology and engineering, for example, for flood forecasting and water management. We develop a model to estimate IDF curves from daily meteorological observations, which are more widely available than the observations on finer timescales (minutes to hours) that are needed for IDF calculations. The method is applied to all data at once, making it efficient and robust to individual errors.