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.

中文翻译：

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评估用于估计强度-持续时间-频率曲线的最大稳定过程的性能

为了明确说明不同累积持续时间的降雨强度最大值之间的渐近相关性，估计强度-持续时间-频率 (IDF) 曲线的最新进展涉及使用最大稳定过程。在我们的研究中，我们旨在估计由 IDF 模型产生的回报水平对性能的影响，该模型解释了这种渐近依赖性。为了研究这种影响，我们使用分位数技能指数 (QSI) 比较了两个 IDF 模型的回报水平估计的性能。一种 IDF 模型基于假设渐近依赖的最大稳定过程；另一种是假设渐近独立性的简化（或减少）持续时间相关 GEV 模型。由此产生的 QSI 表明两个模型的整体性能非常相似，最大稳定模型在短时间内（d≤10h）略微优于其他模型。从模拟研究中，我们得出结论，如果可以假设模型的渐近相关性被正确捕获，则在关注短持续时间时，最大稳定过程值得考虑用于 IDF 曲线估计。