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

TL;DR

This paper introduces a high-resolution spatio-temporal stochastic model for urban extreme rainfall, combining realistic marginal behavior with flexible dependence structures, to improve flood risk assessment.

Contribution

It proposes a novel model that captures both moderate and extreme rainfall events without threshold selection, incorporating explicit advection effects and a new composite likelihood estimation.

Findings

Model accurately reproduces observed extreme rainfall patterns.

Enables realistic stochastic scenario generation for flood risk assessment.

Incorporates explicit advection and flexible dependence in the modeling of extreme events.

Abstract

Modeling precipitation and its accumulation over time and space is essential for flood risk assessment. In this paper, we analyze rainfall data collected over several years through a micro-scale precipitation sensor network in Montpellier, France. A novel spatio-temporal stochastic model is proposed for high-resolution urban extreme rainfall and combines realistic marginal behaviour and flexible dependence structure. Marginally, rainfall intensities are described by the Extended Generalized Pareto Distribution (EGPD), capturing both moderate and extreme events without threshold selection. Based on peaks-over-threshold theory for spatial processes, dependence during extreme episodes is modeled by an r-Pareto process with a non-separable variogram allowing for episode-specific advection, such that the displacement of rainfall cells is represented explicitly. Based on a catalog of extreme space-time episodes extracted from observations, parameters are estimated by a new composite likelihood based on joint exceedance indicators. Empirical advection velocities are derived beforehand from a radar reanalysis dataset. We show that 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.