The tales that the distribution tails of non-Gaussian autocorrelated processes tell: Studying the k-length block-maxima distribution

I. Tsoukalas, The tales that the distribution tails of non-Gaussian autocorrelated processes tell: Studying the k-length block-maxima distribution, 2021, (in review).

[doc_id=2094]

[English]

Focal point of this work is the estimation of the distribution of maxima without the use of classic extreme value theory and asymptotic properties, which may not be ideal for hydrological processes. The problem is revisited from the perspective of non-asymptotic conditions, and regards the so-called exact distribution of block-maxima of finite-sized k-length blocks. First, we review existing non-asymptotic approaches/models, and also introduce an alternative and faster model. Next, through simulations and comparisons (using asymptotic and non-asymptotic models), involving intermittent processes (e.g., rainfall), we highlight the capability of non-asymptotic approaches to model the distribution of maxima with reduced uncertainty and variability. Finally, we discuss an alternative use of such models that concerns the theoretical estimation of the multi-scale probability of obtaining a zero value. A useful finding when the scope is the multi-scale modeling of intermittent hydrological processes (e.g., intensity-duration-frequency models). The work also entails step-by-step recipes and an R-package.

PDF Full text (6864 KB)

Tagged under: Extremes, Stochastics