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.

翻译：对降水及其在时间和空间上的累积进行建模对于洪水风险评估至关重要。本文分析了法国蒙彼利埃OMSEV观测站通过微尺度降水传感器网络多年收集的降雨数据。我们提出了一种新颖的时空随机模型，用于高分辨率城市降雨建模，该模型结合了真实的边际行为和灵活的极值相依结构。降雨强度由扩展广义帕累托分布（EGPD）描述，无需阈值选择即可同时捕捉中度和极端事件。基于空间极值理论，极端事件期间的相依性通过r-帕累托过程建模，该过程采用包含事件特定平流的非分离变异函数，从而能够显式表示降雨单元的位移。参数通过基于联合超阈值的复合似然法进行估计，经验平流速度则源自雷达再分析数据。该模型准确再现了蒙彼利埃OMSEV网络观测到的极端降雨时空结构，并能为洪水风险评估生成真实的随机情景。