Abstract
To explicitly account for asymptotic dependence between rainfall intensity maxima of different accumulation duration, a recent development for estimating Intensity-Duration-Frequency (IDF) curves involves the use of a max-stable process. In our study, we aimed to estimate the impact on the performance of the return levels resulting from an IDF model that accounts for such asymptotical dependence. To investigate this impact, we compared the performance of the return level estimates of two IDF models using the quantile skill index (QSI). One IDF model is based on a max-stable process assuming asymptotic dependence; the other is a simplified (or reduced) duration-dependent GEV model assuming asymptotic independence. The resulting QSI shows that the overall performance of the two models is very similar, with the max-stable model slightly outperforming the other model for short durations (d≤10h). From a simulation study, we conclude that max-stable processes are worth considering for IDF curve estimation when focusing on short durations if the model’s asymptotic dependence can be assumed to be properly captured.
Highlights
Much research has been recently done on the application of multivariate methods to estimateIntensity-Duration-Frequency (IDF) curves
The resulting quantile skill index (QSI) shows that the overall performance of the two models is very similar, with the max-stable model slightly outperforming the other model for short durations (d ≤ 10 h)
The present paper introduces a scheme to evaluate the impact of the asymptotic dependence between rainfall intensities aggregated over different durations on the performance of extreme value theory (EVT)-based
Summary
Much research has been recently done on the application of multivariate methods to estimateIntensity-Duration-Frequency (IDF) curves. IDF curves model a relationship between intensities of extreme rainfall events and their frequencies (i.e., return periods) as a function of event duration. Work on the topic estimates extreme value distributions individually for several fixed durations and subsequently fits an empirical relation to quantiles (return levels) as a function of duration [1,2,3,4]. This approach is prone to inconsistencies as the natural ordering of quantiles is not guaranteed to be preserved over all durations (in other words: quantiles cross). Ritschel et al [8] used this model to characterize stochastic precipitation models, and Ulrich et al
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