Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1017/eds.2022.4
Licence creative commons licence
Title (Primary) Modeling and simulating spatial extremes by combining extreme value theory with generative adversarial networks
Author Boulaguiem, Y.; Zscheischler, J. ORCID logo ; Vignotto, E.; van der Wiel, K.; Engelke, S.
Source Titel Environmental Data Science
Year 2022
Department CHS
Volume 1
Page From e5
Language englisch
Topic T5 Future Landscapes
Data and Software links
Keywords Climate model simulations; extreme value theory; generative adversarial networks; spatial extremes
Abstract Modeling dependencies between climate extremes is important for climate risk assessment, for instance when allocating emergency management funds. In statistics, multivariate extreme value theory is often used to model spatial extremes. However, most commonly used approaches require strong assumptions and are either too simplistic or over-parameterized. From a machine learning perspective, generative adversarial networks (GANs) are a powerful tool to model dependencies in high-dimensional spaces. Yet in the standard setting, GANs do not well represent dependencies in the extremes. Here we combine GANs with extreme value theory (evtGAN) to model spatial dependencies in summer maxima of temperature and winter maxima in precipitation over a large part of western Europe. We use data from a stationary 2000-year climate model simulation to validate the approach and explore its sensitivity to small sample sizes. Our results show that evtGAN outperforms classical GANs and standard statistical approaches to model spatial extremes. Already with about 50 years of data, which corresponds to commonly available climate records, we obtain reasonably good performance. In general, dependencies between temperature extremes are better captured than dependencies between precipitation extremes due to the high spatial coherence in temperature fields. Our approach can be applied to other climate variables and can be used to emulate climate models when running very long simulations to determine dependencies in the extremes is deemed infeasible.
Persistent UFZ Identifier
Boulaguiem, Y., Zscheischler, J., Vignotto, E., van der Wiel, K., Engelke, S. (2022):
Modeling and simulating spatial extremes by combining extreme value theory with generative adversarial networks
Environ. Data Sci. 1 , e5 10.1017/eds.2022.4