Spatio-temporal modeling of urban extreme rainfall events at high resolution

2026-02-23

AI Summary

This study addresses the challenge of coherently modeling both moderate and extreme precipitation intensities and their spatial dependence structures in high-resolution urban extreme rainfall events. Leveraging data from the Montpellier OMSEV microscale rain gauge network, we propose a novel spatiotemporal stochastic model that eliminates the need for threshold selection by integrating the extended generalized Pareto distribution (EGPD) with an r-Pareto process. For the first time within an extreme-value framework, the model explicitly incorporates an event-specific advection-driven nonseparable variogram to capture the dynamic displacement of rainfall cells and spatial extremal dependence. By jointly estimating parameters via a composite likelihood approach above threshold and utilizing advection velocities derived from radar reanalysis, the model successfully reproduces the spatiotemporal structure of regional extreme rainfall, enabling the generation of high-fidelity stochastic scenarios to support robust urban flood risk assessment.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• 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: User modeling for targeted and personalized online advertising

Abstract

Modeling precipitation and its accumulation over time and space is essential for flood risk assessment. We here analyze rainfall data collected over several years through a microscale precipitation sensor network in Montpellier, France, by the OMSEV observatory. A novel spatio-temporal stochastic model is proposed for high-resolution urban rainfall and combines realistic marginal behavior and flexible extremal dependence structure. Rainfall intensities are described by the Extended Generalized Pareto Distribution (EGPD), capturing both moderate and extreme events without threshold selection. Based on spatial extreme-value theory, dependence during extreme episodes is modeled by an r-Pareto process with a non-separable variogram including episode-specific advection, allowing the displacement of rainfall cells to be represented explicitly. Parameters are estimated by a composite likelihood based on joint exceedances, and empirical advection velocities are derived from radar reanalysis. The model accurately reproduces the spatio-temporal structure of extreme rainfall observed in the Montpellier OMSEV network and enables realistic stochastic scenario generation for flood risk assessment.

Problem

• spatio-temporal modeling
• extreme rainfall
• urban precipitation
• flood risk assessment
• extremal dependence

Innovation

• spatio-temporal stochastic model
• Extended Generalized Pareto Distribution
• r-Pareto process
• extreme-value theory
• advection-aware modeling