An Attention-Based Stochastic Simulator for Multisite Extremes to Evaluate Nonstationary, Cascading Flood Risk

Abstract: Compound flood risks from spatially and temporally clustered extremes challenge traditional risk models and insurance portfolios that often neglect correlated risks across regions. Spatiotemporally clustered floods exhibit fat-tail behavior, modulated by low-frequency hydroclimatic variability and large-scale moisture transport. Nonstationary stochastic simulators and regional compound event models aim to capture such tail risk, but have not yet unified spatial and temporal extremes under low-frequency hydroclimatic variability. We introduce a novel attention-based framework for multisite flood generation conditional on a multivariate hydroclimatic signal with explainable attribution to global sub-decadal to multi-decadal climate variability. Our simulator combines wavelet signal processing, transformer-based multivariate time series forecasting, and modified Neyman-Scott joint clustering to simulate climate-informed spatially compounding and temporally cascading floods. Applied to a Mississippi River Basin case study, the model generates distributed portfolios of plausibly clustered flood risks across space and time, providing a basis for simulating spatiotemporally correlated losses characteristic of flood-induced damage.