Non-stationarities in extreme hourly precipitation over the Piave Basin, northern Italy

TL;DR

This paper analyzes the seasonal and spatial non-stationarities in extreme hourly precipitation over the Piave Basin, emphasizing the importance of modeling both marginal and dependence structures for accurate risk estimation.

Contribution

It introduces a comprehensive approach that models non-stationarity in both marginal distributions and dependence structures of extreme precipitation, using covariate-dependent and max-id models.

Findings

Seasonal patterns affect both marginal and dependence structures.

Spatial dependence weakens with increasing extremity.

Joint modeling of marginal and dependence non-stationarities improves risk estimates.

Abstract

We study the spatio-temporal features of extremal sub-daily precipitation data over the Piave river basin in northeast Italy using a rich database of observed hourly rainfall. Empirical evidence suggests that both the marginal and dependence structures for extreme precipitation in the area exhibit seasonal patterns, and spatial dependence appears to weaken as events become more extreme. We investigate factors affecting the marginal distributions, the spatial dependence and the interplay between them. Capturing these features is essential to provide a realistic description of extreme precipitation processes in order to better estimate their associated risks. With this aim, we identify various climatic covariates at different spatio-temporal scales and explore their usefulness. We go beyond existing literature by investigating and comparing the performance of recently proposed covariate-dependent models for both the marginal and dependence structures of extremes. Furthermore, a flexible max-id model, which encompasses both asymptotic dependence and independence, is used to learn about the spatio-temporal variability of rainfall processes at extreme levels. We find that modelling non-stationarity only at the marginal level does not fully capture the variability of precipitation extremes, and that it is important to also capture the seasonal variation of extremal dependence.