Simulating multivariate hazards with generative deep learning

Abstract. When natural hazards coincide or spread over large areas they can create major disasters. For accurate risk analysis, it is necessary to simulate many spatially resolved hazard events that capture the relationships between extreme variables, but this has proved challenging for conventional statistical methods. In this article, we show that deep generative models offer a powerful alternative method for creating sets of synthetic hazard events due to their ability to implicitly learn the joint distribution of high-dimensional data. Our framework combines generative adversarial networks with extreme value theory to construct a hybrid method that captures complex dependence structures in gridded multivariate weather data and provides a theoretical justification for extrapolation to new extremes. We apply our method to model the co-occurrence of strong winds, low pressure, and heavy precipitation during storms in the Bay of Bengal, demonstrating that our model learns the spatial and multivariate extremal dependence structures of the underlying data and captures the distribution of storm severities. Validation shows excellent preservation of spatial correlation structures (r = 0.977, MAE = 0.053) and multivariate dependencies (r = 0.817, MAE = 0.096) for wind, precipitation, and pressure fields. In a case study of storm risk to mangrove forests, we demonstrate that correctly modelling the dependence structures leads to far more realistic estimates of aggregate damages. While our method shows mild underestimation of the damages with a mean absolute error of 93.57 km2, this remains an order of magnitude lower than errors from independence assumptions (460.54 km2) and the total dependence assumption (1056.90 km2) that is implicit when using return period maps. The framework developed in this paper is flexible and applicable across a wide range of data regimes and hazard types.