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

2025-11-25

AI Summary

This study addresses the challenge of modeling spatiotemporal nonstationarity in extreme hourly precipitation over the Piave River Basin in northeastern Italy. We find that both the marginal distribution and spatial extremal dependence structure exhibit pronounced seasonality, with dependence weakening as extremity increases—rendering conventional approaches (which only render marginals nonstationary) inadequate. To overcome this, we propose a novel framework jointly modeling nonstationary marginals and seasonally varying spatial extremal dependence using a covariate-driven max-infinitely divisible process. This approach unifies asymptotic dependence and independence regimes and integrates multiscale climate covariates with statistical learning techniques. Results demonstrate substantial improvements in the accuracy of extreme precipitation risk estimation. The framework provides a more realistic and robust paradigm for regional hydrometeorological extreme-value modeling, enabling better-informed flood risk assessment and climate adaptation planning.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Analyzing non-stationary extreme hourly precipitation patterns
• Investigating seasonal variations in marginal and dependence structures
• Modeling climatic covariates to improve extreme risk estimation

Innovation

• Covariate-dependent models for marginal and dependence structures
• Flexible max-id model for spatio-temporal extremes
• Incorporating climatic covariates at multiple scales