Spatial modeling of rare events has obvious applications in the environmental sciences and is crucial when assessing the effects of catastrophic events (such as heatwaves or widespread flooding) on food security and on the sustainability of societal infrastructure. Although classical geostatistics is largely based on Gaussian processes and distributions, these are not appropriate for extremes, for which max-stable and related processes provide more suitable models. This paper provides a brief overview of current work on the statistics of spatial extremes, with an emphasis on the consequences of the assumption of max-stability. Applications to winter minimum temperatures and daily rainfall are described.
|Original language||English (US)|
|Number of pages||19|
|State||Published - Jul 2013|
Bibliographical noteFunding Information:
Acknowledgements This paper is based on an invited lecture at GeoEnv2012. We thank the organizers, and particularly Jaime Gómez-Hernández, for their splendid hospitality, and reviewers for helpful comments on an earlier version of this paper. The work was supported by the Swiss National Science Foundation and by the ETH domain Competence Center Environment and Sustainability (http://www.cces.ethz.ch/). The data were kindly supplied by MétéoSuisse.
- Asymptotic independence
- Brown-Resnick process
- Gaussian process
- Generalised Pareto distribution
- Max-stable process
- Statistics of extremes
ASJC Scopus subject areas
- Mathematics (miscellaneous)
- Earth and Planetary Sciences(all)