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A depth-duration-frequency model for analysis of extreme precipitation events, with application to past and projected future climates in Ireland

O'Brien, Enda
Wang, Jingyu
Ryan, Páraic
Nolan, Paul
Mateus, Carla
Citation
O'Brien, Enda, Wang, Jingyu, Ryan, Páraic, Nolan, Paul, & Mateus, Carla. (2026). A depth-duration-frequency model for analysis of extreme precipitation events, with application to past and projected future climates in Ireland. Weather and Climate Extremes, 51, 100862. https://doi.org/10.1016/j.wace.2026.100862
Abstract
A simple depth-duration-frequency (DDF) model is proposed to reveal the asymptotic characteristics of extreme but short-lived precipitation events that satisfy a peak over threshold (POT) size criterion. Our objective is to reliably estimate the return periods for events of a given intensity (as measured by rainfall depth and duration). For each depth threshold and duration period, the number of qualifying POT events is simply counted over multi-year periods, whether from observations or model output. The distribution of events as a function of their size above the threshold is modelled by a generalized Pareto distribution (GPD), following standard extreme value theory. Those exceedance distributions are shown, to a good approximation, to be independent of location. This justifies the aggregation of exceedances from multiple locations, which is a key feature of the model. Aggregation acts as a data multiplier, enabling more reliable estimation of GPD fits and return periods. The model is applied to intense precipitation observations spanning 30–64 years at 23 stations in Ireland. Three-hourly output from an ensemble of global climate simulations, downscaled to high-resolution over Ireland, were also used to compute both historical and projected future intense event return periods under two different emission scenarios. Future numbers of events per time-period are projected to increase by 20–80 %, depending on event threshold and duration, location, emission scenario and time-period. Return periods are projected to shorten by factors of 2 or more for the most intense events, as illustrated by return period maps for events of any given size.
Publisher
Elsevier
Publisher DOI
Rights
CC BY
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