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

• Published in: Hydrology and earth system sciences Vol. 27; no. 20; pp. 3719 - 3732
• Main Authors: Parding, Kajsa Maria, Benestad, Rasmus Emil, Dyrrdal, Anita Verpe, Lutz, Julia
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 20.10.2023; Copernicus Publications
• Subjects: Analysis; Bayesian analysis; Bayesian theory; Case studies; Climate change; Daily rainfall; Empirical analysis; Empirical models; Extreme values; Extreme weather; Gauges; Hourly rainfall; Hydrology; Local climates; Precipitation; Principal components analysis; Probability theory; Rain; Rain and rainfall; Rain gauges; Rainfall; Rainfall intensity; Rainfall intensity duration frequencies; Statistical analysis; Statistical inference; Statistics; Norway
• ISSN: 1607-7938; 1027-5606; 1607-7938
• DOI: 10.5194/hess-27-3719-2023

Intensity–duration–frequency (IDF) statistics describing extreme rainfall intensities in Norway were analysed with the purpose of investigating how the shape of the curves is influenced by geographical conditions and local climate characteristics. To this end, principal component analysis (PCA) was used to quantify salient information about the IDF curves, and a Bayesian linear regression was used to study the dependency of the shapes on climatological and geographical information. Our analysis indicated that the shapes of IDF curves in Norway are influenced by both geographical conditions and 24 h precipitation statistics. Based on this analysis, an empirical model was constructed to predict IDF curves in locations with insufficient sub-hourly rain gauge data. Our new method was also compared with a recently proposed formula for estimating sub-daily rainfall intensity based on 24 h rain gauge data. We found that a Bayesian inference of a PCA representation of IDF curves provides a promising strategy for estimating sub-daily return levels for rainfall.